EUROPEAN COMMISSION DG RTD - ASSESS Project Deliverables/ASSESS D… · Deliverable No. ASSESS D2.2...

EUROPEAN COMMISSION

DG RTD SEVENTH FRAMEWORK PROGRAMME

THEME 7 TRANSPORT - SST

SST.2008.4.1.1: Safety and security by design GA No. 233942

ASSESS Assessment of Integrated Vehicle Safety Systems for improved

vehicle safety

Deliverable No. ASSESS D2.2 (2/2)

Deliverable Title Socio-economic impact of safety systems

Dissemination level Public PU

Written By Jan-André Bühne (BAST) Andreas Lüdeke (BAST) Susanne Schönebeck (BAST) Jan Dobberstein (UOC) Helen Fagerlind (CHALMERS) András Bálint (CHALMERS) Mike McCarthy (TRL),

2012-06-29

Checked by Nils Lubbe (TOYOTA) Maminirina Ranovona (TOYOTA) Thomas Unselt (DAI) Carmen Rodarius (TNO) Paul Lemmen (Humanetics) Jean-Francois Boissou (PSA)

Approved by Paul Lemmen (Humanetics)

Issue date 2012-06-29

ASSESS D2.2 – Socio-economic impact of safety systems Public

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Acknowledgement Following participants contributed to this deliverable report. Company Representative Chapters BAST Andreas Lüdeke All chapters BAST Jan-André Bühne All chapters BAST Susanne Schönebeck All chapters UOC Jan Dobberstein All chapters CHALMERS Helen Fagerlind Chapter 3 CHALMERS András Bálint Chapter 3 TRL Mike McCarthy Chapter 3


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Executive summary

The aim of the ASSESS project is to develop test and assessment methods for Integrated vehicle safety systems (IVSS) which combine elements of active and passive safety. In this project, methods for forward-looking collision avoidance and mitigation systems for passenger cars are being developed. A critical aspect of WP2 (Socio economic evaluation and legal aspects) of the ASSESS project is measuring the benefits conferred by the forward-looking safety system. The objective is to provide a thorough assessment of the safety impacts. Therefore, a monetary valuation of the safety benefits was carried out. However, since a reliable estimation of system cost is currently very difficult a Break-Even Analysis (BEA) is carried out instead of the classical cost-benefit approach in order to calculate critical safety system cost. Pre-crash functionalities use components of other safety systems such as adaptive cruise control and thus a separation of costs is not possible and connected with some uncertainties due to highly confidential cost information of OEMs. Cost data from other projects like eIMPACT are quite old and do not fit very well to the bandwidth of functionalities of current pre-crash systems and their difference in performance. Since the BEA follows the principles of a classical CBA in order to assess the monetary safety benefit of pre-crash systems, the calculated critical safety system costs represent target break-even costs, which indicate the maximum cost of the system that would still provide societal benefits and therefore would comply with a benefit-cost ratio of more than one. The main results of the socio-economic assessment can be summarized as follows:

• In the first part of the report background data which are needed for socio-economic assessment are provided. This includes the forecast of fatality figures for the assessment period 2020 and 2030, and a forecast on the passenger car fleet based on ProgTrans data. The estimated fatality trend shows (medium scenario) between 2010 to 2020 a reduction of nearly 35 %, and for the next decade (2020 – 2030) a further fatality reduction of about 9 %. For the European car fleet a growth rate of about 10% between 2010 and 2020 and of about 9% between 2020 and 2030 was estimated.

• Because safety benefits of the system depend on the participation of the equipped cars in traffic, the share of equipped cars was transferred to the share of mileage driven by equipped cars. Since age is a strong predicator of annual car mileage, by using German car market data a linear regression analysis of a functional relationship between (age-dependent) share of car fleet and corresponding (age-dependent) share of mileage driven was specified.

• The core of the safety assessment is a mitigation model from WP1 based on the concept of injury risk curves formalising the relationship between fatality / injury risk and delta-v. In the model a lognormal distribution of accidents over impact speed parameter delta-v is used following US-accident data because similar data at this level of detail are missing for Europe. This accident distribution is used as input for estimation of an impact of the pre-crash system on the accident distribution function. Combined with injury-risk functions then percentage reductions of casualties are predicted. These changes are interpreted as system effectiveness with respect to different reductions of delta-v caused by the pre-crash system. Based on the testing results the mean safety impact of the pre-crash system accounts for an decrease of nearly 55% of fatalities, 29% serious injuries and 20% slight injuries in rear-end accidents.


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• For the socio-economic assessment generic pre-crash system variants including warning and autonomous emergency braking were assumed. Based on the results from the safety impact analysis a range of critical safety system costs have been calculated. For the mean scenario with an assumed market penetration of the car fleet with pre-crash systems of 5% in 2020 and 15% in 2030, a reduction of fatality figures ranging from 31 to 34 (2020) and from 51 to 54 (2030) was calculated for the different system variants. The maximum total safety benefit in the mean scenario accounts for nearly 124 million Euro in 2020 and 278 million Euro in 2030 respectively. This results in critical safety system costs of 93 (2020) and 64 (2030) Euro which represent manufacturing cost of the system. Taking the well established “Factor 3” rule of thumb into account market prices for the pre-crash systems could be in the range of 280 (2020) and 192 (2030) Euro and still be efficient from an overall society point of view.

• The sensitivity of results is addressed at three different levels, starting from parameter changes of the calculation model (market penetration, accident data forecast), then taking a full equipment scenario for 2020 and 2030 into account and finally, enlarging the model to incorporate further safety impacts due to other avoided accidents than only rear-end collisions. For changes of the accident trend and market penetration forecasts the calculated critical safety system costs show depending on the parameter changes a range of46-121 Euro in 2020 and 34-88 Euro in 2030. Enlargement of the safety benefit assessment with findings from the eIMPACT project show a considerably decrease in total benefits and target break even cost. Critical safety cost amount to 256 Euro in 2020 due to systems effectiveness on other accident types than only rear-end crashes.

• The applied Break-even analysis methodology has proven its applicability to this type of research question. Up-scaling from micro level (testing) to macro level (EU-27 databases for accidents etc.) provides still considerable challenges, especially concerning the granularity of information. Socio-economic assessment makes typically use of averages of variables whereas distributions of variables would be valuable to keep the value added of testing data. Research in this direction would help to solidify the derivation of socio-economic impacts from testing data. Nevertheless, it is demonstrated how the safety assessment tool and the calculated critical system costs can be used to rank pre-crash systems of different functionalities (warning system vs. purely autonomous pre-crash system etc.).


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Contents

1 Introduction .................................................................................................................... 8

2 Methodology and Assessment framework .....................................................................10

2.1 Scope of socio-economic impact analysis in ASSESS ...........................................10

2.2 Accident data forecast ............................................................................................11

2.3 Forecast of accident target population of rear-end collisions ..................................15

2.4 Vehicle fleet and mileage data ...............................................................................16

2.4.1 Forecast of vehicle fleet ..................................................................................16

2.4.2 Forecast of driven mileage ..............................................................................16

3 Safety impact assessment .............................................................................................20

3.1 Safety impact assessment approach of ASSESS ...................................................20

3.2 Concept of using injury-risk functions in safety impact assessment ........................22

3.2.1 Injury risk functions and their requirements .....................................................22

3.2.2 Existing injury risk functions ............................................................................22

3.2.3 Selection of risk function .................................................................................27

3.2.4 Limitations of injury risk functions ....................................................................27

3.2.5 Recommendations for safety impact assessment ............................................28

3.3 Safety impact assessment in ASSESS ...................................................................29

3.3.1 The applied procedure ....................................................................................30

3.3.2 Example calculation ........................................................................................34

3.3.1 Separate assessment for autonomous action and driver reaction ...................36

4 Socio-economic assessment .........................................................................................37

4.1 General procedure and assumptions of the socio-economic assessment ...............37

4.2 Variants of Break-even analysis in ASSESS project ...............................................38

4.3 Cost-unit rates ........................................................................................................39

4.4 Vehicle cost and vehicle fleet .................................................................................41

4.5 Break-even analysis (test based) – BEA I and BEA II.............................................43

4.5.1 Benefit assessment for mean scenario ............................................................43

4.5.2 Critical safety system costs for mean scenario ................................................44

4.6 Sensitivity analysis .................................................................................................45

4.6.1 Scenario analysis for accident data and market penetration forecast ..............46

4.6.2 Extended Break-even analysis (BEA III) ..........................................................47

5 Conclusions ...................................................................................................................50

6 Risk Register .................................................................................................................52

7 References ....................................................................................................................53


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Tables Table 2-1: Results of fatality estimation for EU-25 in eIMPACT (Wilmink et al. 2008) ...........11

Table 2-2: Forecast on fatalities for EU-27 and country cluster, optimistic approach (Eurostat 2011, own calculations) ......................................................................................13

Table 2-3: Forecast on fatalities for EU-27 and country cluster, pessimistic approach (Eurostat 2011, own calculations) ......................................................................14

Table 2-4: Forecast on fatalities for EU-27 and country cluster, mean accident trend (Eurostat 2011, own calculations) ......................................................................15

Table 2-5: Rear-end accident target population for EU-27, mean scenario (own calculation) ...........................................................................................................................16

Table 2-6: Vehicle stock of passenger cars for EU-27, million vehicles (ProgTrans 2010, Eurostat 2011, *own calculation) ........................................................................16

Table 2-7: Assumed penetration rates and corresponding share of fleet of equipped passenger cars (own calculations) .....................................................................19

Table 3-1: Comparison of available injury risk curves (green = fulfilled, orange = partly fulfilled; red = not fulfilled) (own figure) ...............................................................27

Table 3-2: Overview on main characteristics of available injury risk curves (own figure) .......27

Table 3-3 Weighting of different test scenarios .....................................................................32

Table 3-4 Closing speed at impact in scenario 3 with no brake reaction ...............................33

Table 3-5 Available points and scored points in Scenario 2 ..................................................35

Table 3-6 Overall safety impact assessment for injury reduction in the subject vehicle.........35

Table 3-7 Overall safety impact assessment for injury reduction in the target vehicle ...........35

Table 3-8 Overall safety impact assessment for the total injury reduction (taking into account both the subject and the target vehicles) ............................................................36

Table 3-9 Overall safety impact assessment for injury reduction with purely autonomous action of the pre-crash system ...........................................................................36

Table 3-10 Overall safety impact assessment for injury reduction with a pre-crash system assuming a warning system that triggers driver reaction with 100% efficiency ...36

Table 4-1: Casualty costs-unit rates in € for 2005 in EU 25 in factor prices (Assing et al. 2006) ..................................................................................................................40

Table 4-2: Cost-unit rates for casualties and injury accidents (€-2011) .................................40

Table 4-3: Overview on cost price data provided by eIMPACT (Baum et al. 2008), SAFESPOT 2010 (Schindhelm et al. 2009) ........................................................41

Table 4-4: Percentage reduction of fatalities and injuries (own calculations) ........................43

Table 4-5: Safety impact in terms of avoided accidents and casualties for the mean accident data forecast and mean penetration rate (own calculations) ...............................44

Table 4-6: Safety benefit in Euro for 2020 and 2030 for the mean accident data forecast and mean penetration rate (own calculations) ...........................................................44

Table 4-7: Critical safety system costs for the mean accident data forecast and mean penetration rate (own calculations) (€-2011) ......................................................45

Table 4-8: Results of sensitivity analysis (market penetration, accident trend) (own calculations) .......................................................................................................47

Table 4-9: Overall target population for all accident types affected by emergency braking system (EBR), EU-27, 2020, 2030 (own calculation, based on Wilmink et al. 2008) ..................................................................................................................48

Table 4-10: Percentage reduction of fatalities and injuries, other accident types than rear-end, 100 %-performance of EBR-system (own calculation, based on Wilmink. et al. 2008) .............................................................................................................48

Table 4-11: Benefit impact and critical system costs for the enlarged BEA III scenario ........49


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Figures

Figure 2-1: Assessment approach for socio-economic analysis in ASSESS. ........................11

Figure 2-2: EU country clusters based on safety performance defined within eIMPACT (Wilmink et al. 2008)...........................................................................................12

Figure 2-3: Forecast on road fatalities for EU-27 and three country, optimistic approach (Eurostat 2011, own calculations) ......................................................................13

Figure 2-4: Forecast on road fatalities for EU-27 and three country cluster, pessimistic approach (Eurostat 2011, own calculations) .......................................................14

Figure 2-5: Forecast on road fatalities for EU-27, 2010 to 2030, three scenarios (Eurostat 2011, own calculations) ......................................................................................15

Figure 2-6: Rate of cars surviving a specific age, normal distribution (own calculations, based on Christidis. et al. 2003) ....................................................................................17

Figure 2-7: Age distribution of share of car fleet and mileage driven by car fleet, Germany, 2010 (Federal Transport Authority, Christidis et al. 2003, own calculations) .......17

Figure 2-8: Relationship between share of car fleet and mileage driven by car fleet for Germany (own calculations) ...............................................................................18

Figure 3-1: Speed injury risk curve for car drivers in frontal impacts with other cars (Richards & Cuerden 2009) (Remark: “W” represents the risk of fatality defined by Wramborg, 2005.) ..............................................................................................23

Figure 3-2: Risk of Injury (MAIS 2+) for drivers of striking vehicles in rear-end crashes by safety belt use (1193 drivers belted and 213 drivers unbelted; MAIS 2+ drivers: 62 belted and 30 unbelted) (Kusano and Gabler 2010) ......................................23

Figure 3-3: Occupant maximum injury as a function of longitudinal Delta-v (152 belted and 27 unbelted front seat occupant) (Gabauer and Gabler 2006) .................................24

Figure 3-4: Probability of MAIS 2+ injury for belted occupants (Hampton and Gabler 2009) 24

Figure 3-5: Probability of MAIS 3+ injury for belted occupants (Hampton and Gabler 2009) 25

Figure 3-6: Risk of MAIS 4+ in frontal crashes by impact severity (Viano and Parenteau 2010) ..................................................................................................................25

Figure 3-7: Injury-risk of passenger car occupant depending on delta-v (Busch 2005) .........26

Figure 3-8: Injury-risk of passenger car occupants depending on delta-v (kph) (Hannawald 2008) ..................................................................................................................26

Figure 3-9 Change of crash frequency and number of injured (own figure)...........................30

Figure 3-10 Crash frequencies for subject and target vehicles in rear-end crashes in the US (Kusano and Gabler 2010). In both graphs, the horizontal axis shows delta-v values (km/h) and the corresponding weighted numbers of accidents are displayed on the y-axis. ......................................................................................31

Figure 4-1: Variants of Break-even analysis based on ASSESS testing (own figure) ............38


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1 Introduction

Road safety is a major societal issue in the European Union. In 2009, more than 35,000 people died on the roads of the European Union, i.e. the equivalent of a medium town, and no fewer than 1,500,000 persons were injured. The cost for society is representing approximately 130 billion Euros in 2009 (EC 2010). Therefore, as described in its “4th Road safety action plan for 2011-2020”, the EU is aiming at raising the level of road safety and ensuring safe and clean mobility for citizens in every region in Europe. The same line of argumentation can be identified in the European Commission’s 2011 Transport White Paper (EC 2011) (Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system). It reinforces the accident reduction goal of the 2001 White Paper, i.e. halving the number of fatalities on EU roads by the end of decade. The goal for this decade is to halve the road casualties (fatalities and injuries) by 2020 while the long term vision towards 2050 should move close to zero fatalities in road transport. Also the automotive industry is strongly committed towards these objectives by developing and equipping vehicles with advanced driver assistance and safety systems. Road safety policy measures should put citizen at the heart of the actions by encouraging them to take primary responsibility for their safety and the safety of other persons. In order to put the customers in the position to make the right decisions about car safety, the knowledge and awareness of the potential benefits of such systems have to be gathered and distributed widely. EuroNCAP is one way to improve the decision-making of customers. Testing procedures and recommendations from the ASSESS project deliver valuable input for EuroNCAP regulations and therefore enhance the overall knowledge level of car drivers about vehicle safety systems. Socio-economic impact assessment in ASSESS constitutes an important part of carrying out the benefit analysis of the considered pre-crash systems. The objective of this deliverable is to inform about the socio-economic dimension of the impacts derived from ASSESS and the costs associated with these technologies. This requires not only information available from testing in but also complementing information on safety and traffic performance in the EU-27 in order to provide the bigger picture of European scale effects. Finally, via a break-even analysis the critical safety system costs of pre-crash system variants are identified which would still guarantee benefits from an overall societal economic point of view. The detailed socio-economic analysis in this deliverable is organized as follows: In Chapter 2 the methodology and assessment framework for the socio-economic analysis in ASSESS is characterised. This includes the forecast of the accident target population for the assessment period 2020 and 2030 on EU-27 level. The accident data forecast updates and extends the eIMPACT accident data forecast for the years 2010/ 2020. Furthermore, the estimation of the passenger car fleet for the aforementioned assessment years based on ProgTrans data and a transfer of this car fleet data to mileage driven by the car fleet based on age structure of the fleet are presented. Given the effectiveness of the pre-crash safety system, safety benefits are a function of the share of the car fleet, and more precisely, the share of the mileage which is driven by cars fitted with the safety system. In Chapter 3 the details of the safety assessment tool which was developed and applied in WP1 of ASSESS are described. As the core of the safety assessment methodology, the specific role of the injury risk function depending on impact speed parameter delta-v is described and its use in a mitigation model is explained. In the model the injury risk relationship is applied to an accident distribution function depending on delta-v. By the use of an elaborated dose-response model the mitigation effects expressed by percentage change


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of casualties are derived for different system variants such as purely autonomous action and a warning system that triggers the driver reaction with 100% efficiency. Chapter 4 comprises the core of the socio-economic assessment. First of all, the chapter shows the cost-unit rates which are used to transfer casualty reductions into monetary safety benefits. In addition, it is explained how the vehicle fleet and mileage is used to calculate critical safety system costs. Based on the findings from the safety impact assessment via a break-even analysis the critical safety system costs for the different generic pre-crash system variants are derived. Finally in a sensitivity and scenario analysis parameter changes like a pessimistic and optimistic accident data forecast are tested for their relevance on the critical safety system costs. Also the assessment scope is broaden by the consideration of potential safety impacts on other accident and collision types than the observed and tested rear-end collisions in ASSESS in order to avoid an underestimation of the benefits. Finally, chapter 6 summarises the main results and provides conclusions for further directions of work.


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2 Methodology and Assessment framework

2.1 Scope of socio-economic impact analysis in ASSESS

The core of the socio-economic study in ASSESS is the identification of critical safety system costs for forward looking crash avoidance and mitigation systems, similar to the concept used in previous socio-economic analysis of advanced primary safety systems conducted by TRL (Grover et al. 2008, Robinson et al. 2011). Usually, socio-economic impact assessment of driver assistance systems is based on a cost-benefit analysis that accounts for all benefits and all cost on a society level and includes all relevant effects on all groups. By appraising benefits and cost in comparable monetary terms, benefits-cost ratios (BCR) provide an easy to understand judgment of road safety measures. However, since a reliable estimation of system cost is currently very difficult a Break-Even Analysis (BEA) is carried out instead of the classical cost-benefit approach in order to calculate critical safety system cost. Pre-crash functionalities use components of other safety systems such as adaptive cruise control and thus a separation of costs is not possible and connected with some uncertainties due to highly confidential cost information of OEMs. Cost data from other projects like eIMPACT are quite old and do not fit very well to the bandwidth of functionalities of current pre-crash systems and their difference in performance. Since the BEA follows the principles of a classical CBA in order to assess the monetary safety benefit of pre-crash systems, the calculated critical safety system costs represent target break-even costs, which indicate the maximum cost of the system that would still provide societal benefits and therefore would comply with a benefit-cost ratio of more than one. However in practice, still various socio-economic assessment approaches exist which might lead to different results. This is mainly because costs and benefits to society are virtual figures and no exact metrics, which are defined by the scope and described in potentials, even though the underlying “real-world effects” are unambiguous data. Therefore, choosing a different definition of the scope may lead to different assessment results, which might still be – within their given scope – all correct. It is important to adjust the scope to the given problem to assess. The general characteristics of the BEA carried out in ASSESS are displayed in Figure 2-1 :

• Main inputs to the socio-economic analysis are input from accidentology data from WP1 such as weighting factors for the different accident types and scenarios as well as the determination of the accident target population on European level for the assessment period 2020 and 2030. These accident data forecast uses updated and extended accident data from the EU’s sixth framework programme project eIMPACT.

• Based on results from the testing work-package the safety benefit of the pre-crash systems for certain scenarios (stopped lead vehicle, slower lead vehicle, braking vehicle) is integrated in an elaborated safety impact model from WP1 which discloses the safety impact in terms of percentage changes in injuries and fatalities. With the help of these percentages the total number of reduced accidents and casualties for EU 27 can be determined.

• In a last step cost-unit rates are applied to the safety impact results. Conducting a socio-economic analysis always means to some extent interpreting potential effects from an economic perspective. Physical impacts – e. g. estimated reduction of accidents and casualties – are transferred into monetary benefits for society. The cost-unit rates used in ASSESS represent the current state of the art in research. These rates come from advanced econometric models and aggregated modelling of economic cost. In combination with estimated market penetration rates for the European car fleet in 2020 and 2030 the BEA can be conducted in order to identify the critical safety system costs.


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Figure 2-1: Assessment approach for socio-economic analysis in ASSESS.

In conclusion, the BEA in ASSESS combines the state-of-the-art of a socio-economic analysis of advanced driver assistance systems from theoretical studies with insights derived from testing results within the WP4 and 5 on changes occurring due to the presence of pre-crash systems.

2.2 Accident data forecast

Since up-to-date forecasts of accidents and/or casualties for the boundaries of the target period 2020 to 2030 are not available on EU-27 level, a regression analysis was carried out for the selected time horizon based on the accident data gathered in the EU project eIMPACT.1 In the eIMPACT project (Wilmink et al. 2008) the road safety prediction for EU-25 was based on the future development of the fatality risk, i. e. the ratio between the total number of fatalities and the total vehicle kilometres driven (see Wilmink et al. 2008). Then the fatality rate trend was calculated for three different country clusters, and by a combination with an estimated trend in country specific vehicle mileage, fatality numbers per country for 2010 and 2020 were calculated, and aggregated to EU-25 level.

Table 2-1: Results of fatality estimation for EU-25 in eIMPACT (Wilmink et al. 2008)

1 The time horizon of 2020 and 2030 was defined in discussions with ASSESS partners. It was

selected such to have one or more vehicle generations in between the timings for the assessment, allowing OEM’s for fitment of the systems in next generations of vehicle models.

WP1: Accidentology

• In-depth accident data

•Ranking of accident types

WP 1 Safety assessment model

•Avoided fatalities and injuries

•Mitigated casualties

•Avoided accidents

Monetary valuation of safety impact

(safety benefits)

• No. of avoided/mitigated

casualties/accidents

•Cost-unit rates

WP4/5: Pre-/Crash test

System performance in each test

scenario

EU-27 target population

•Forecast of fatalities & injuries if

system 100% effective

(2)

SAFETY

ASSESSMENT

(3)

MONETARY

VALUATION

(1)

ACCIDENT

DATA BASE

Estimated market penetration rates

•Car fleet data

•Scenarios: low, regular, high

Break Even-Analysis

Critical safety system costs (2020/2030)

2010 2020

Cluster 1 8,683 5,430

Cluster 2 14,878 9,283

Cluster 3 10,334 6,077

Sum EU-25 33,895 20,791


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The eIMPACT approach is also used here, but further refined. First a regression analysis was carried out to extrapolate the trend of fatality rates to the ASSESS assessment horizon 2020 and 2030. The calculation of the fatality rates is based on the ProgTrans World Transport Report 2010 / 2011 data on vehicle mileage (ProgTrans 2010) and Eurostat data on fatalities in the 27 member states for the period 2000 to 2009. For each year the fatality rate was calculated. The forecast is done for EU-27 and, similar to eIMPACT, for three country clusters which are defined based on road safety performance in 25 member states. The map below shows the European member states which were distributed by eIMPACT into three country clusters according to indicators for road safety performance. Countries which are categorized to have a high road safety performance are coloured “yellow” (Cluster 1), countries with an intermediate road safety performance are coloured “orange” (Cluster 2), and countries with a low road safety performance are coloured “red” (Cluster 3). The new member states Bulgaria and Romania are allocated to the low performance countries due to their constantly high, and up to now not decreasing fatality rates.

Figure 2-2: EU country clusters based on safety performance defined within eIMPACT (Wilmink et al. 2008).

In ASSESS – different to eIMPACT - two procedures for forecasting fatality trends were used:

• Optimistic approach assuming fatality trend is strongly driven by the large countries with regard to fatality numbers, hence the continuously improving safety performance over the last decade will continue to increase in similar extent, due to the


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mechanisms of road safety measures like improvements in driver education, a better traffic law enforcement, safer vehicles, and a safer road infrastructure.

• Pessimistic approach – compared to the first approach – reflecting more differences in progress in safety performance over the last decade. On aggregated cluster level, there is convergence towards a fatality rate close to the actual observed fatality rate. Hence a weighted time series comes to higher fatality numbers for 2020 and 2030.

• Mean accident trend as the average of the optimistic and pessimistic accident data forecast.

Optimistic forecast If the same future development for all countries per cluster is assumed the accident trend exploration is strongly influenced by the fatality trend in countries with high population and relatively high absolute fatality numbers, for example by Germany and UK in Cluster 1, by France, Italy, and Spain in Cluster 2, and by Poland, and Romania in Cluster 3. Especially the road safety trend of these countries in Cluster 1 and 2 was quite positive over the last decade. Therefore, the so called optimistic forecast shows a fatality reduction of about 45% from 2010 to 2020, and a reduction of about 49% for the next decade (2020 - 2030). So, given this trend fatality reduction will continue on a quite high level.

Table 2-2: Forecast on fatalities for EU-27 and country cluster, optimistic approach (Eurostat 2011, own calculations)

Figure 2-3: Forecast on road fatalities for EU-27 and three country, optimistic approach (Eurostat 2011, own calculations)

Pessimistic forecast Following this approach a fatality trend is calculated separately in each country cluster for the countries with the largest number of fatalities: Cluster 1: Germany, UK; Cluster 2: France, Italy, Spain; Cluster 3: Poland, Romania. Otherwise the trend of these countries would have dominated the fatality trend of the smaller countries. For the rest of the countries of each cluster with lower absolute numbers of fatalities an aggregated fatality trend is calculated.

2010 2020 2025 2030

EU-27 33,675 18,211 13,030 9,321

Cluster 1 7,941 4,454 3,308 2,457

Cluster 2 12,392 6,367 4,490 3,164

Cluster 3 13,342 7,390 5,231 3,699


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Because of quite strong differences in the fatality rate of the residual countries of cluster 3 this residual group was further divided into two groups (Cluster 3a: Cyprus, Czech Republic, Portugal, Slovenia; Cluster 3b: Bulgaria, Greece, Hungary, Latvia, Slovakia). The fatality rates for the 7 countries with the highest number of fatalities and the calculated fatality rate trend of the remaining countries in each cluster are then used for forecasting the EU-27 trend and the trend for the three country cluster. Table 2-3 shows the forecast results for fatalities at three time points, and Figure 2-2 the fatality trend. From 2020 to 2030 the forecast done here shows a reduction of nearly 25% for the pessimistic scenario, and for the next decade (2020 - 2030) of 7%. So fatality reduction continues, but on a much lower level compared to the optimistic approach.

Table 2-3: Forecast on fatalities for EU-27 and country cluster, pessimistic approach (Eurostat 2011, own calculations)

Figure 2-4: Forecast on road fatalities for EU-27 and three country cluster, pessimistic approach (Eurostat 2011, own calculations)

Mean accident trend The above shown two variants of forecasts of accident trends create a range of expectable developments of road safety in Europe. However, for safety impact assessment a mean scenario is chosen, and the pessimistic and optimistic approach serve as boundary values for sensitivity analysis.

Table 2-4 shows the average values of the forecast results for the pessimistic and optimistic scenario and Figure 2-5 the corresponding mean fatality trend. The mean trend shows a reduction of nearly 35% for EU-27 from 2010 to 2020, and a reduction of 29% for the next decade from 2020 to 2030. So, fatality reduction continues, but on a lower level compared to the optimistic approach and on a higher level compared to the pessimistic approach.

2010 2020 2025 2030

EU-27 34,280 26,212 23,795 22,070

Cluster 1 8,146 6,322 5,821 5,455

Cluster 2 12,671 9,343 8,448 7,812

Cluster 3 13,463 10,547 9,526 8,803

ASSESS D2.2 – Socio-economic impact of safety systems

Table 2-4: Forecast on fatalities for EU(E

Figure 2-5: Forecast on (E

Figure 2-5 also shows the target of the 3(EC 2003). The 3rd and also the 4state the target of halving the number fatalities in the first decade, and respectively of the second decade of the century. The target of the 327.000) was not completely reached. The reduction in fatalities from 2000 to 2010 amounts to nearly 40%. The target of the 4trend, and especially with regard to the pessimistic forecast approach (reduction of road fatalities to about 17.000 in 2020)

2.3 Forecast of accident target popu

Ranking of accident scenarios of WP1 has shown the high relevance of rearTherefore, the socio-economic assessment of WP2 focuses on this collision type. Furthermore, focus on rear end collisions is driven by the fact that technology for this collision type is available and governments and consumer organisations (NCAP’s) worldwide start developing regulations / protocols for system scenarios. The accident data of the eIMPACT project was used as casualty trends to 2020 and 2030. eIMPACT numbers and casualties for different accident types, including rearare used for the definition of the accident target population trend estimation shown above was used to calculate upthe rear-end collision casualties and injdata for 2020 and 2030. The following table shows the ASSESS for rear-end collisions

EU-27

Cluster 1

Cluster 2

Cluster 3

economic impact of safety systems

Forecast on fatalities for EU-27 and country cluster, mean (Eurostat 2011, own calculations)

: Forecast on road fatalities for EU-27, 2010 to 2030, three scenarios(Eurostat 2011, own calculations)

hows the target of the 3rd European Road Safety Action Program of 2003and also the 4th European Road Safety Action Program (RSAP) of 2010

the number fatalities in the first decade, and respectively of the second decade of the century. The target of the 3rd RSAP (reduction of road fatalities to

was not completely reached. The reduction in fatalities from 2000 to 2010 amounts The target of the 4th RSAP is also ambitious with regard to the mean accident

trend, and especially with regard to the pessimistic forecast approach (reduction of road fatalities to about 17.000 in 2020) (EC 2010).

Forecast of accident target population of rear-end collisions

Ranking of accident scenarios of WP1 has shown the high relevance of reareconomic assessment of WP2 focuses on this collision type.

focus on rear end collisions is driven by the fact that technology for this collision type is available and governments and consumer organisations (NCAP’s) worldwide start developing regulations / protocols for system approval / rating related to these

eIMPACT project was used as a basis for forecastto 2020 and 2030. eIMPACT also provides disaggregated

numbers and casualties for different accident types, including rear-end collisiondefinition of the accident target population in ASSESS. Therefore, the fatality

above was used to calculate up-scaling factors. These are applied to end collision casualties and injury accidents for 2005 to calculate

The following table shows the accident target populaend collisions:

2010 2020 2025 2030

33,977 22,211 18,412 15,695

8,044 5,388 4,565 3,956

12,531 7,855 6,469 5,488

13,402 8,968 7,379 6,251

Public

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ean accident trend

three scenarios

European Road Safety Action Program of 2003 rogram (RSAP) of 2010

the number fatalities in the first decade, and respectively of the (reduction of road fatalities to

was not completely reached. The reduction in fatalities from 2000 to 2010 amounts RSAP is also ambitious with regard to the mean accident

trend, and especially with regard to the pessimistic forecast approach (reduction of road

end collisions

Ranking of accident scenarios of WP1 has shown the high relevance of rear-end collisions. economic assessment of WP2 focuses on this collision type.

focus on rear end collisions is driven by the fact that technology for this collision type is available and governments and consumer organisations (NCAP’s) worldwide

/ rating related to these

basis for forecasting accident and disaggregated data on accident

nd collisions. These data Therefore, the fatality

scaling factors. These are applied to calculate the corresponding

accident target population of

15,695

3,956

5,488

6,251


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Table 2-5: Rear-end accident target population for EU-27, mean scenario (own calculation)

In rear-end collisions whiplash injuries play a dominant role. They are the most important kind of injury connected to rear-end accidents which causes a costly burden on the insurance industry. Therefore, slight injuries play a very important role in rear-end collisions. This is reflected in the very high rate of slight injuries in comparison to fatal injuries of nearly 160 for this collision type. Over all collision types this rate is only round about 33.

2.4 Vehicle fleet and mileage data

In this chapter vehicle data connected to the cost side and benefit side of the BEA is estimated. To calculate the critical safety system costs of the different safety systems the total vehicle fleet of passenger cars has to be estimated for the assessment period of 2020 to 2030. In addition, for the estimation of the safety benefits the mileage driven by the equipped cars is estimated as an important measure to predict realistic safety impacts of these cars. 2.4.1 Forecast of vehicle fleet

Data on the vehicle stock is provided by the ProgTrans World Transport Report Edition 2010/2011 (ProgTrans 2010). The report includes data from all EU-27 member states. The report entails a forecast of the stock of passenger cars for 2020 and 2025 but not for the year 2030. Therefore, the value for 2030 was estimated by regression analysis based on ProgTrans and Eurostat data about passenger car fleet.

Table 2-6: Vehicle stock of passenger cars for EU-27, million vehicles (ProgTrans 2010, Eurostat 2011, *own calculation)

2.4.2 Forecast of driven mileage

In order to calculate the safety benefits, the simple share of equipped passenger cars does not give an accurate prediction of the real safety impacts of the systems. Normally the mileage of new passenger cars is higher than the mileage of older cars. Thus the fleet penetration of new equipped cars has to be converted into the share of mileage by using data about the age structure of the car fleet combined with the age distribution of annual car kilometers (Wilmink et al., 2008). The weighting is based on the economic development and aging of the car fleet. The age structure of the German car market is used here. Every year the number of newly registered cars is published. An estimate of the average car age and the average survival time is also available. However, data on the age distribution of the car stock in detail is not available as default analysis.

Year

Fatal

accidents Injury accidents Fatalities

Serious

injuries

Slight

injuries

2005 1,089 127,850 1,089 14,398 158,927

2020 599 76,461 599 8,328 94,682

2030 422 59,549 422 6,829 81,200

2005 2010 2020 2025 2030*

EU-27 221,119 240,327 264,195 271,902 288,498

Cluster 1 84,675 90,799 98,832 102,068 107,114

Cluster 2 96,275 102,426 112,102 115,107 121,091

Cluster 3 38,018 47,103 53,263 54,728 60,182


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Figure 2-6: Rate of cars surviving a specific age, normal distribution (own calculations, based on Christidis. et al. 2003)

Therefore, a hazard rate based on a JRC-Paper is used to estimate the number of cars not surviving the next year (Christidis et al. 2003). For determining the age of leaving the fleet (cross order sales, wreckage), the normal distribution allows to depict the survival rate of all cars as used in numerous similar fleet and car market models (Figure 2-6). Thus the net number of cars of a specific age is estimated by multiplying the new car registrations of certain years with the age specific survival rate. Then, these net numbers of vehicles are multiplied with the mileage driven at a certain age (Christidis et al. 2003). By determining the share of the fleet and the share of the vehicle mileage of a certain vehicle age, a regression analysis delivers the functional relationship between fleet and traffic penetration from these data points.

Figure 2-7: Age distribution of share of car fleet and mileage driven by car fleet, Germany, 2010 (Federal Transport Authority, Christidis et al. 2003, own calculations)

Hazard rate


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Figure 2-7 shows the age distribution of the share of the fleet and the corresponding share of the mileage driven by this fraction of the fleet. Very new cars, younger than five years old, drive more than their corresponding share of the fleet. For example, cars of the age of one year have a fleet share of 9 %, but drive about 15 % of the mileage. For cars about five years old, fleet share and mileage share are nearly identical. For cars older than five years it holds, that fleet share is larger than mileage share, but diminishing with age. Figure 2-8 plots the relationship between the share of the fleet and the corresponding share of mileage. For example, it is estimated that about 50 % of the mileage of the car fleet is driven by a smaller share of the fleet, by about 40 %. The figure shows that the difference between share of mileage and the share of fleet decrease if the share of the fleet, and thus the average age of this share of the car fleet, increases.

Figure 2-8: Relationship between share of car fleet and mileage driven by car fleet for Germany (own calculations)

To establish the relationship between car age, passenger car fleet share and mileage share German car data was used as data for all European countries were not available on the needed level of detail. However, the average car age of 8.3 years in Germany (2008) equals the European average (ACEA 2010). Available European data on age distributions show that 34 % (Germany: 40%) of the car fleet is less than 6 years old, and 32 % is between 6 and 10 years old (Germany: 37%). Therefore, the estimation shown here may overestimate the share of mileage driven by the share of new cars. Since only those accidents are affected where the subject vehicle in an accident is equipped with the system, the fleet penetration rate and the share of mileage driven by the equipped fleet are used to estimate the expected impact in 2020 and 2030. Currently (2011), fitment of passenger cars with emergency braking systems is suggested to be below 1 %. Usually market deployment of passenger cars with new technology like driver assistance and safety systems starts with luxury cars and then is cascading down to lower segments of the car market. Because a sharp drop of end-user prices is not expected for the short term a relatively low penetration rate (and corresponding share of mileage) is assumed for the mean scenario of assessment. The assumptions about market penetration rates are similar to estimations of the eIMPACT study for an emergency braking system. The higher rates are quite similar to the observed market deployment of an innovative safety system like Electronic Stability Control which was cascading down to lower segments of the market relatively fast. The Lower (Min) and higher (Max) rates are also used for sensitivity analysis. Table 2-7 shows the penetration rate for the mean penetration scenario and the minimum and maximum values as well as the corresponding share of car mileage driven by the equipped vehicles. The difference between


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the number of equipped cars and the vehicle mileage is caused by the fact that new cars, which usually have a higher mileage, are the ones equipped with an pre-crash system.

Table 2-7: Assumed penetration rates and corresponding share of fleet of equipped passenger cars (own calculations)

Min 3% Min 6.75%

Mean 5% Mean 10.01%

Min 10% Min 17.08%

Mean 15% Mean 23.35%

Max 30% Max 39.84%

Max 10% Max 17.08%

...in traffic 2020

…in traffic 2030

Penetration

of fleet

2030

Penetration

of fleet

2020


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3 Safety impact assessment

This chapter shows the methodology and its application for the estimation of safety impacts of forward looking crash avoidance and mitigation systems (in the following called pre-crash systems). The safety impact methodology proposed here uses results of testing in ASSESS for assessment. The main safety impact of pre-crash systems due to the mechanisms triggered by the systems (warning, brake enforcement, autonomous emergency braking) is the result of the reduction in impact speed. The core methodology stated therefore is about linking system test results with regard to impact speed reduction to an estimate of casualty reduction. In this report, safety impact assessment is limited to accidents and casualties of rear-end collisions, since this is in the focus of the current pre-crash systems to be tested in ASSESS. This is in line with developments worldwide by Governments and NCAP organisations given by the fact that technology is available and entering the market for these scenarios. ASSESS safety impact assessment is based on the methodology developed and applied in WP1 and documented in the forthcoming deliverable D1.4 (Bàlint et al. 2012). The ASSESS impact assessment approach from WP 1 wants to provide an assessment tool for decision makers from national and European policy, consumer protection organisations or public domain rating organisations for the interpretation of test results and the estimation of the safety potential of the tested systems. The process towards an applicable assessment model makes use of accident data – both direct target group filtering and speed-related injury risk analysis – and testing capabilities that allow to determine system effectiveness and performance in line with realistic conditions. Chapter 3.1 and 3.2 lay focus on the approach and the data used in order to translate ASSESS test results to a potential effect that can be applied on the estimated accident target population (see 2.3). Chapter 3.3 explains in detail how the methodology from WP 1 is applied in order to generate the reduction of casualties in terms of percentages for the categories slight and serious injuries as well as fatalities.

3.1 Safety impact assessment approach of ASSESS

The safety impact assessment in ASSESS aims at estimating the collision avoidance and mitigation potential of safety systems. Because the assessment addresses safety impacts of innovative safety systems with a level of market deployment which has just started, ex-post accident analysis, e.g. by a case-and-control approach, is not possible. Thus a predictive model which applies available results on system effectiveness on the forecasted accident target population is necessary. In principle, such a model can use expert estimations, simulation results, and/ or results from FOTs and pre-/ crash-tests as data source for estimation of system effectiveness. The different methods used by theoretical and empirical approaches, with respect to data generation for impact assessment, can be characterised as follows:

• Theoretical approaches: The safety impact is determined by using elaborated physical models and simulations. Theoretical approaches can further include the usage of R&D data or tests and vehicle safety expert analyses rebuilding the stochastic devolution of the different crash phases which allow an estimation of safety impacts by application of reliable and validated previous scientific results. By desktop research, the already available data and results are aggregated, for example to the statement that system “A” can reduce x % of crashes of collision type “Y”. Desktop analysis can include empirical results e.g. based on driver simulator studies, pre-crash and crash tests.


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• Empirical approaches: Usually the internal validity of theoretical approaches, i.e. the researcher’s ability to formulate accurate conclusions about the cause-and-effect relationships, will not be very high. The quality of the data acquisition depends on the sampling techniques (e.g. random sampling) and the research design (e.g. case and control approach) used. Of course, for the case of an ex-post assessment, the method of accident data analysis is also available, which allows to find and to prove correlations between the introduction of a safety measure in the past and the reduction in accident statistics. Statistical proof states the ideal scientific impact assessment, since the uncertainty of uncontrolled influences could be assumed to be low and the data situation to be reliable. However, for innovative vehicle safety systems, making use of this approach is not (yet) possible.

The ASSESS approach combines theoretical and empirical research approaches. In the context of the ex-ante assessment of ASSESS, the integration of empirical data in the assessment improves the situation considerably, compared to assessments only based on desktop analysis. To improve internal validity of the safety impact assessment, empirical foundation by small and large-scale FOTs, laboratory studies on driving simulators and test tracks are required. It is important that the actual impact (accident avoidance) is empirically observed. Nevertheless, no direct test of the systems in the field is done and the future impacts have to be predicted, which means that the assessment still is theoretical research. Safety impact in terms of accident and injury reduction in ASSESS correlates the performance of safety systems tested in controlled test scenarios and the supposedly adjunct injury risk in real world accidents. The key parameters and inputs are the following:

• Accident target population: any benefit of a safety system can only be derived from addressable accidents – in ASSESS, rear-end accidents represented by “A scenarios”. Accident databases (especially on EU level) are limited in their options to filter out relevant accidents, hence available sources require up-scaling. The eIMPACT-based target group forecast is described in chapter 2.3, more detailed findings on the characteristics of the accidents addressed by the ASSESS systems are documented in the deliverables of WP1 (D1.1: McCarthy et al. 2010, D1.2: Wisch et al. 2010).

• Injury risk functions: The main safety impact of pre-crash systems due to the mechanisms triggered (warning, brake enforcement, autonomous emergency braking) is the reduction in impact speed. Linking system test results with regard to impact speed reduction to an estimate of casualty reduction requires an applicable functional relationship processed from relevant accident data.

• Accident distribution over impact speed: Beside the relative injury risk at certain impact speeds or speed reductions, the importance of speed ranges depends on the frequency of accidents occurring at these speeds. Especially if system performance was lower at very high speeds, the overall assessment may reflect these differences in a reasonable way by estimating the importance.

• Test results: one of ASSESS key objectives is to evaluate the various approaches (track test, driver simulator) to determine the effectiveness of safety systems in order to provide a “test tool box” that may support future traffic and vehicle safety strategies. WP2 aligned with WP1 and WP4 aimed at making use of these results by forming an assessment framework around it.


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3.2 Concept of using injury-risk functions in safety impact assessment

3.2.1 Injury risk functions and their requirements

In order to estimate system effectiveness for the target population, a way of quantifying the reduction in injury risk is required. This relationship can then be used to estimate the reduction in fatal, serious and slight casualties in the target population. A range of injury risk relationships have been published so far. This section is intended to present the options and to discuss the important points relating to the injury risk functions. The existing data typically use injury risk against delta-v. The speed parameter delta-v defines the change in vehicle velocity associated with primary force of the crash event. It depends on relative driving speed of the vehicles, relative masses, and crash absorptive capacity of vehicles (Hannawald 2008). Delta-v is a better predictor of injury than other measures of speed, such as impact speed or closing speed because it includes the stiffness and weight characteristics of the vehicle in addition to the speeds of the vehicles involved. It is necessary to check that the data used to derive the injury risk function can be applied appropriately. The inclusion and exclusion criteria applied to the data are important to understand whether the function can be applied. In particular, it is important to consider the following points:

• To which vehicles types and what fleet age the data relate;

• To which impact conditions (frontal, side) the data relate;

• To which overlap the data relate;

• Which exclusion criteria are used (e.g. belted occupants etc.). 3.2.2 Existing injury risk functions

ASSESS will present information on a range of existing injury risk functions which have been identified as candidates for use in the estimation of system effectiveness range of injury risk functions. These are summarised in the following section. When comparing different injury risk functions, one should note the difference in the units of measurement of Delta-v. Richards and Cuerden 2009 Sample (CCIS 1983+; OTS 2000+; GB accidents) includes 64 fatally injured belted drivers (CCIS) in frontal impacts with another car; 463 seriously injured belted drivers (OTS) in frontal impacts with another car. STATS19 was used to determine the average number of car driver casualties in frontal impacts from 2005–07, by injury severity. These figures were used to weight the results from CCIS and OTS. The figure shows the risk for fatality and the aggregate risk to be killed or seriously injured over a period of time (KSI). The dotted lines show the confidence intervals (95%) of the risk curves.


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Figure 3-1: Speed injury risk curve for car drivers in frontal impacts with other cars (Richards & Cuerden 2009) (Remark: “W” represents the risk of fatality defined by

Wramborg, 2005.)

Kusano and Gabler 2010 Sample (1993-2008) includes 1,406 rear-end striking vehicles from NASS2 / CDS3. Impacts with car, light truck or van are included.

Figure 3-2: Risk of Injury (MAIS 2+) for drivers of striking vehicles in rear-end crashes by safety belt use (1193 drivers belted and 213 drivers unbelted; MAIS 2+ drivers: 62

belted and 30 unbelted) (Kusano and Gabler 2010)

2 National Automotive Sampling System

3 Crashworthiness Data System


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Gabauer and Gabler 2006 Sample of 191 frontal collision vehicles from NASS4 / CDS5 and EDR6 database. Subject vehicles were GM vehicles with airbag deployment.

Figure 3-3: Occupant maximum injury as a function of longitudinal Delta-v (152 belted and 27 unbelted front seat occupant) (Gabauer and Gabler 2006)

Hampton and Gabler 2009 Sample includes 1,086 crashes (1,899 vehicles from NASS7 / CDS8) from 2006. Sample comprises all impact types.

Figure 3-4: Probability of MAIS 2+ injury for belted occupants (Hampton and Gabler 2009)

4 National Automotive Sampling System


6 Event Data Recorder 7 National Automotive Sampling System



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Figure 3-5: Probability of MAIS 3+ injury for belted occupants (Hampton and Gabler 2009)

Viano and Parenteau 2010 Sample (model year 1997+) was taken from NASS / CDS. Target vehicles were light vehicles. Frontal crashes were selected.

Figure 3-6: Risk of MAIS 4+ in frontal crashes by impact severity (Viano and Parenteau 2010)

Figure 3-7 is based on GIDAS-data for frontal collisions. Only passenger cars are included. The cars included are equipped with Airbag, and the car occupants are belted. The used classification for casualty seriousness differentiates between four levels: fatal, serious, slight and not injured. The curves show the probability for different injury levels of car occupants in frontal collision which sum up to 1.0. The curves define four regions for the four injury levels. For example, the region below the lowest curve shows the probability in case of collision to be uninjured


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respectively the curve above shows the probability to be slightly injured or uninjured. Thus the difference between both curves gives the probability to be slightly injured etc. For example, given a level of delta-v of 54 kph the probability to be uninjured amounts to 0.066, to be slightly injured 0.422, to be seriously injured 0.478, and to be fatally injured 0.034. If a safety system reduces delta-v to 47 kph, for example, probability to be seriously injured changes from 0.478 to 0.344.

Figure 3-7: Injury-risk of passenger car occupant depending on delta-v (Busch 2005)

The curves in Figure 3-7 do not show the well-known S-curve shape of the usual injury-risk curves above, simply because the curves present the probabilities for the maximal possible injury level reached given delta-v. On contrary, Hannawald 2008 shows based on logistic regression of GIDAS data S-curves for the probability of minimal injury levels of belted car occupants: fatality injured, to be at least seriously injured, and to be at least slightly injured.

Figure 3-8: Injury-risk of passenger car occupants depending on delta-v (kph) (Hannawald 2008)


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3.2.3 Selection of risk function

In order to compare the risk curves and decide which ones are most appropriate, the following tables compare the above shown risk curves. This considers whether the risk curve is applicable for fatal, serious and slight injuries and whether or not the sample is based on frontal impacts or contains only cars as subject and target vehicles of the conflict.

Table 3-1: Comparison of available injury risk curves (green = fulfilled, orange = partly fulfilled; red = not fulfilled) (own figure)

Injury risk curve Fatal Serious Slight Frontal impacts

Cars only

RICHARDS & CUERDEN, 2009

KSI

KUSANO & GABLER, 2010

MAIS2+ subject vehicle

GABAUER & GABLER, 2006

MAIS3+

HAMPTON & GABLER, 2009

MAIS3+ MAIS2+

VIANO & PARENTEAU, 2010

MAIS4+

BUSCH, 2005

HANNAWALD, 2008

Table 3-2: Overview on main characteristics of available injury risk curves (own figure)

Injury risk curve Injury severity Approx risk @ 30km/h

Approx risk @ 40km/h

Approx risk @ 60km/h

RICHARDS and CUERDEN, 2009

Fatal 0% 1% 10%

RICHARDS and CUERDEN, 2009

KSI 2% 80% 100%

KUSANO & GABLER, 2010

MAIS 2+ 1% 5% 30%

GABAUER & GABLER, 2006

MAIS 3+ 2% 15% 70%


MAIS 2+ 7% 25% 75%


MAIS3+ 2% 9% 33%

VIANO & PARENTEAU, 2010

MAIS 4+ 0% 1% 10%

BUSCH, 2005 Fatal / serious / slight, injury

0% / 12% / 60% 2 - 3% / 30% / 45%

3% / 60% / 30%

HANNAWALD, 2008

Fatal / serious / slight, injury

1% / 9% / 62% 2% / 16% / 66% 4% / 40% / 50%

3.2.4 Limitations of injury risk functions

The injury risk function is, by definition, the average relationship between risk of injury and delta-v based on a particular sample of casualties and accidents. In reality, the individual injury risk is influenced by a multitude of other factors: occupant age, occupant biometrics, vehicle age, passive safety performance of the vehicle. Also absolute vehicle speed probably


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plays a role, as it may influence the risk of loss of control and secondary crash. Although these variables will affect the injury risk, it is reasonable, and indeed necessary, to select an average relationship which takes into account the most dominant variables. The selected injury risk function should be the one which provides the most accurate representation for the type of safety feature under assessment. Some injury risk functions (including Richards 2010) are based on a sample containing only injury accidents and do not include uninjured occupants. The effect of this exclusion is that the injury risk estimated from the sample is overestimated at very low delta-v values (<15 mile/h). The presented injury risk functions relate to delta-v, whereas the ASSESS test data records impact speed. However, if delta-v is not directly measured, it can be calculated. Therefore, there is also a need to relate the impact speed reduction in the test to delta-v for assessment of the pre-crash performance. 3.2.5 Recommendations for safety impact assessment

For safety impact assessment and socio-economic assessment injury-risk curves based on Hannawald 2008 were used. The following aspects summarise the reasons for its choice:

• Curves are based on GIDAS accident and injury data of frontal impacts and this is the relevant crash type for ASSESS.

• The data base includes only cars, and mini buses.

• Consistent and constant curves are provided for fatal, serious, slight and uninjured casualties. In accidentology MAIS levels are more common, but for the purpose of up scaling of safety impacts to high level national and European accident data bases MAIS levels are not directly usable.

• The final injury risk curves need to take into account and build a methodologically correct estimate for rear-end collisions only (e.g. dividing the complete risk curve by a constant factor) or just use accumulated empirical curves.

There are different speeds that could be of relevance for ASSESS testing as well as for the estimation of potential safety benefits in this WP2:

• Speed difference between subject and opponent vehicles, a couple of seconds before the crash (e.g. before any mitigation action by the driver).

• Speed difference between subject and opponent vehicles, immediately before the crash (after mitigation actions).

• Delta-v: the difference between the speed of the vehicle considered, during the crash (e.g. difference between the speed immediately before the shock, and the residual speed after energy absorption and interaction with the opponent vehicle).

But the chosen definition for delta-v may not be the easiest one to use, as there is no straightforward link with vehicle dynamics. Experts sometimes use EES (Equivalent Energy Speed), e.g. in testing passive safety measures, but the availability in accident data does not support this approach. There is no data to link EES with probabilities of injuries / fatalities. The ASSESS rear-end test scenarios are defined with regard to impact speed. The injury risk curves are defined for reconstructed delta-v. Therefore, it is assumed that the safety impact assessment for pre-crash aspects can be estimated using rules (factors) relating impact speed to delta-v. In the safety impact assessment, we make the simplifying assumption that delta-v equals half the speed difference between the subject and opponent vehicles immediately before the crash. This way we can see both the crash frequency and the injury


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risk as functions of the former. For example, a 20 kph impact speed reduction due to autonomous braking that can be observed in a test would lead to a 10 kph delta-v reduction. The above assumption is valid in certain rear-end crashes (such as the case of 100% overlap, no restitution, equal mass of vehicles) and research results indicate that essentially this relationship between delta-v and collision speed difference may be the best linear approximation available. For the assessment method, it is essential that delta-v is related to collision speed difference by a constant factor, but the actual value of the constant is of secondary importance; the method could be relatively easily modified to take other factors into account (e.g. different factors for the subject and target vehicle and different factors in different scenarios could also be allowed).

3.3 Safety impact assessment in ASSESS

Safety impact assessment in ASSESS is performed by a procedure that correlates the performance of vehicles tested in controlled environments and the change in their risk exposure in the real world. The objective of this procedure is to give guidelines on how to associate specific test scenarios and test results to a reduction in the number of certain injuries. The main objective is to estimate the number of fatalities and severe and slight injuries saved / mitigated in a certain population if the vehicle fleet was equipped with a pre-crash system with a certain performance. It can be used to estimate the generic real-world benefit of the pre-crash system in question. The main principles underlying this procedure are summarized in this section, and a detailed description of the method will be given in deliverable D1.4 (Bálint et al. 2012). The introduction of a pre-crash system with a certain performance into the fleet changes the risk exposure of a certain population. The so-called dose-response model is proposed as a tool to estimate the injury-reducing effects of this change. In the model, both crash frequency and injury severity of a sample of real accidents, classified by delta-v during impact, are taken into account for both the impacted and the impacting vehicles. This combination of frequency and severity for the whole range of speeds provides the relative number of injured classified by impact speed. By this, it is possible to obtain the relative number of injured at any impact speed. This information is enough to determine:

• Weighting among each of the tests: it is given by the relative total number of injured in that scenario in the relevant speed range in the impacting and the impacted vehicles.

• Performance of the system in a specific test: it is given by the percentage of relevant injuries prevented by shifting from the starting speed of the scenario and the actual impact speed obtained during the test.

The next image gives an example of the change of the crash frequency and, when combined with the injury risk, the relative number of injured, for a given sample of accidents when a pre-crash system decreasing the impact speed by 10 km/h in every crash is considered. The crash frequency here is a rescaled version of a lognormal approximation of NASS/CDS data while the injury risk curve is from Hannawald 2008.


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Figure 3-9 Change of crash frequency and number of injured (own figure)

Note that the crash frequency curve in the figure is shifted to the left by 5 km/h. That is because, as mentioned earlier, it is assumed that delta-v during impact equals half the speed difference between the impacted and impacting vehicles at the time of collision.

3.3.1 The applied procedure

In this section it is explained how the dose-response model is used to estimate the benefit of a pre-crash system, based on test results. This section is concerned with the underlying principles while an example in the next section will make it easier to understand these concepts. Injuries adressed A separate assessment is given in each of three test scenarios, described below, for the following injury types (including both subject and target vehicles):

• at least slight injury;

• at least serious injury;

• fatal injury. As stated before, for safety impact assessment, injury-risk curves based on Hannawald 2008 were used, but other injury risk curves (with the corresponding crash frequencies) could be used instead without changing the method. The injury risk curves in Hannawald 2008 are concerned with the risk of the above three types of injury for occupants of the subject vehicle. In order to estimate the injury reduction for the target vehicle occupants as well, it is necessary to retrieve the corresponding injury risk functions for the target vehicle for each injury type. Since these injury functions do not seem to be available for all injuries considered, the estimates in this chapter were derived under the assumption that the relationship between the injury risk functions in the different

0

20

40

60

80

100

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100

% f

or

inju

ry r

isk

, n

um

be

r o

the

rwis

e

Change of velocity (km/h)

Crash frequency

without speed

reduction

Crash frequency if

impact speed

reduced by 10 km/h

Risk of at least

serious injury

Number of injured

without speed

reduction

Number of injured if

impact speed

reduced by 10 km/h


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vehicles is comparable to the case of the MAIS2+ injury risk curves in Kusano and Gabler 2010, where the injury curves are available for the subject and the target vehicles separately. More precisely, it was assumed that at every delta-v value, the ratio between the injury risk in the target and subject vehicles equals the corresponding ratio for MAIS2+ injuries in Kusano and Gabler 2010. Crash frequency functions In addition to the selection of an adequate injury-risk function, the dose-response model needs reliable data on the frequency and hence the distribution of accidents with respect to delta-v. This data is necessary to describe the share of accidents which hypothetically can be addressed by speed reduction within the accident target population. Unfortunately, European numbers on the occurrence of crashes and injuries at this detailed level are insufficient since in-depth databases do not contain enough cases. Therefore, the US crash frequency distribution in Kusano and Gabler 2010, extracted from NASS/CDS data will be used in the safety impact assessment here. The main reason for this choice is that separate crash frequencies are available for the subject and the target vehicles, which improves the quality of the predictions. A study conducted with US accident data (Kononen et al., 2010) shows how the empirical distribution of a large number of accidents over “delta-v” follows a lognormal distribution with a high reliability. This motivated our choice to approximate the Kusano-Gabler curves with a lognormal distribution as well (see Figure 3-10). It is then hypothetically assumed that these approximations show with high reliability a frequency of accidents over delta-v similar to European crash data.

Figure 3-10 Crash frequencies for subject and target vehicles in rear-end crashes in the US (Kusano and Gabler 2010). In both graphs, the horizontal axis shows delta-v

values (km/h) and the corresponding weighted numbers of accidents are displayed on the y-axis.

Scenarios The test scenarios that the safety impact assessment is based on are different from the ASSESS test scenarios in order to suit the dose-response method better, as well as to be more in line with developments worldwide by Governments and NCAP organisations. Therefore, extra care will be required when deciding which ASSESS results are meaningful for the safety impact assessment. This problem will be discussed in detail in the next sections. The following three scenarios are considered:

0

20000

40000

60000

80000

100000

120000

0 20 40 60 80 100

US subject vehicles

Lognormal

approximation

0

10000

20000

30000

40000

50000

60000

0 20 40 60 80 100

US target vehicles

Lognormal

approximation


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• Scenario 1: Stopped lead vehicle, vehicle under test driving at 10-80 km/h;

• Scenario 2: Slower lead vehicle driving 20 km/h, vehicle under test driving at 50-80km/h;

• Scenario 3: Braking lead vehicle, different headway and deceleration conditions. The weighting among these is determined by accident data (in this case, from the ASSESS analysis in GIDAS) as shown in the following table:

Table 3-3 Weighting of different test scenarios

Scenario Weight

Scenario 1 45%

Scenario 2 24%

Scenario 3 31%

100%

Incremental speed approach in test scenarios 1-2 An incremental speed approach is used in test scenarios 1-2. This requires testing each scenario by 5 kph increments in the initial speed of the vehicle under test. This means that each scenario will be tested several times, with different initial speeds, and the result of a test is the impact speed in case of a collision or the fact that the crash has been avoided. Each of the tests will have a pair of points (Vinitial, Vimpact), with Vimpact defined as 0 in case of avoidance. The procedure proposed here will estimate the benefit of the performance of the vehicles in each specific test:

• Within a chosen scenario, each test, with a certain Vinitial, has a certain weighting when compared with other tests with a different Vinitial. This is due to the fact that, potentially, each Vinitial addresses a certain relative number of injured. This can be called ‘available points’.

• Each test result, defined by Vimpact, relates to a certain level of performance. This performance can be quantified by how much the impact speed reduction decreases the number of injuries. This can be called ‘scored points’.

The main advantage of the available points – scored points system is that it gives an idea of what the benefit of speed reduction in each speed range is, as opposed to just providing only one number (i.e. the overall injury reduction) based on all test results. In this sense, we need to identify:

• available points for each of the tests at different initial speeds, based on the potential maximum relative frequency of injuries addressed at that speed;

• scored points for the performance in each of the tests at different speeds, based on the injury risk decrease.

These values can be defined for the whole range of speeds and both for impacting and impacted vehicles with the corresponding injury risk and crash frequency functions; we refer the reader to the deliverable D1.4 (Bálint et al. 2012) for details.


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Test scenario 3: braking lead vehicle This scenario differs from the others in that the vehicle under test has a constant initial speed of 50 km/h. The analogy with the incremental speed approach is provided by the varying headway and braking conditions of the lead vehicle, which also is driving at 50 km/h initially. It is not difficult to compute what the speed difference at the time of collision between the two vehicles would be without braking of the vehicle under test (see the table below). Then, it is possible to estimate delta-v during impact, after which the available points for the different tests may be derived as in scenarios 1-2.

Table 3-4 Closing speed at impact in scenario 3 with no brake reaction

Test number within Scenario 3

Headway of lead vehicle

(m)

Deceleration of lead vehicle

(m/s2)

Velocity difference right before

collision without system (km/h)

Test 1 12 2 25

Test 2 12 6 43

Test 3 40 2 46

Test 4 40 6 50

The scored points are again determined by the injury-reducing effect of decreasing the speed of the vehicle under test from 50 km/h to Vimpact, where the benefit is computed using the dose-response model. Human-machine interaction (HMI) It is well known that warning systems are important components of pre-crash systems hence the scored points should be influenced by driver behaviour. Therefore, for each scenario and each test speed, two tests are proposed:

• Test with no driver reaction, which delivers Vimpact with purely autonomous action of the pre-crash system.

• Test with a brake robot system, which monitors the warning signal and triggers a brake action, with a certain force and time profile representing a real driver. The impact speed in this test is denoted by Vdriver.

The expected number of injuries is then computed separately (with the dose-response model) for Vimpact and Vdriver, and a weighted average of these expected numbers will be used to compute the reduction of injuries for the test in question. It is important to note that it would be insufficient to conduct only the test with driver reaction since only a certain percentage of drivers react to warning, and it is this ratio which should determine the weighting between the above expected numbers. It was decided to use the values from the driving simulator studies described in Aoki et al. 2010 for the weighting, where the ratio of drivers reacting to warning was around 80%.


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3.3.2 Example calculation

In this section, an example is given that demonstrates the process of correlating test results with injury reduction, using scaling factors and rounding for simplification. The test results in the example will be derived from actual ASSESS test results, as described below. Principles for choosing test results As mentioned earlier, the test scenarios here are not the same as those in ASSESS. In particular, the speed increment method applied here with small (5 kph) increments is much better suited to the dose-response method since it provides a nearly complete picture about how the crash frequency function is transformed by the pre-crash system. However, it is not trivial how to derive realistic test results at all test speeds from the ASSESS tests which are performed at only a few different initial speeds. The following two-step procedure, which may be repeated for each scenario, is a natural way to tackle this problem.

1. Choose the most relevant ASSESS test, i.e., the one in which the conditions are the same or very similar to those in one of the tests within the chosen scenario. For example, for Scenario 2, the ASSESS tests A1A1 and A1A3 are most relevant as A1A1 is similar to the test at Vinitial = 50 km/h in Scenario 1 with autonomous action (the difference being that the speed of the target vehicle is 10 km/h in the ASSESS test and 20 km/h in Scenario 2) while A1A3 is the test at the same initial speeds with fast driver reaction. The tests relevant for Scenario 1 are A3A1 and A3A3, while those relevant for Scenario 3 are A2A1, A2A3, A2B1 and A2B3.

2. Assume that the impact speed reductions take the same absolute or relative value at all speeds as that in the chosen test. For example, if the observed Vimpact equals 30 km/h in the test with Vinitial = 50 km/h, then we may assume the same absolute reduction (i.e. 20 km/h) or the same relative reduction (40%) for all tests. After a consultation with experts, it was decided to use the same absolute reduction since this principle shows a higher degree of similarity with the way current systems work.

A further problem arises from the fact that the tests were performed in 3 labs on 4 different vehicles. The number of repeats in the tests was varied as well. Hence, it is not immediately clear what “the observed Vimpact” means in step 2. Which values to use was decided on a case-by-case basis, taking into account the special circumstances in each case, as described in the next section. Test results chosen For Scenario 1, the impact speed reduction in each test with autonomous action was chosen to be the mean impact speed reduction in the ASSESS test A3A1 over all labs and vehicles A, B and C, namely 9.23 km/h. (Numerical data about the ASSESS test results cited here may be found in the appendix of deliverable D4.3). Vehicle D is excluded since there are no tests in A3A3 with this vehicle and, as an extreme outlier in A3A1, it would make the impact speed reduction without driver reaction much higher than that with driver reaction, which may not be realistic. The impact speed reduction in tests with driver reaction, 9.16 km/h, is the mean value for A3A3 tests (vehicles A, B, C). For Scenario 2, as indicated above, the relevant tests in ASSESS are A1A1 for the autonomous action and A1A3 for the one with driver reaction. Here, vehicles A, B, C and D are all taken into account. For Scenario 3, the ASSESS tests used are A2A1 and A2A3 (vehicles A, B, C) and A2B1 (only vehicle B) and A2B3 (only vehicle A). Since these ASSESS tests give values to only 2 of the 4 tests in Scenario 3, the same impact speed reductions were assumed in the remaining two tests.


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Available and scored points within a scenario This example is concerned with the injury reduction benefits for the subject vehicle in Scenario 2, but similar tables are available for both the subject and the target vehicle in each scenario. The following table shows the number of available and scored points at all test speeds as computed using the dose-response model, rounded to the closest integer.

Table 3-5 Available points and scored points in Scenario 2

Scores are then summed separately for different injury types for all tests in the scenario. Finally, each sum is divided by total number of available points for the corresponding injury type, and the performance for each injury type is the resulting ratio expressed as a percentage. Overall assessment Having estimated the injury reduction separately for each scenario, the overall benefit of the pre-crash system is a weighted average of performances, with weights determined by accident data (see weighting among scenarios 1-3 in Table 3-3). This resulted in the following estimated injury reductions for the subject vehicle:

Table 3-6 Overall safety impact assessment for injury reduction in the subject vehicle

The corresponding benefit estimates for the target vehicle are somewhat lower:

Table 3-7 Overall safety impact assessment for injury reduction in the target vehicle

Test

number

Test speed

(km/h):

Impact speed of

subject vehicle,

autonomous

(km/h)

Impact speed of

subject vehicle,

with driver reaction

(km/h):

Available

points

Scored

points

Available

points

Scored

points

Available

points

Scored

points

1 50 30 22 9 6 9 6 1 1

2 55 35 27 10 5 10 5 6 6

3 60 40 32 9 4 10 5 10 10

4 65 45 37 8 3 9 5 8 8

5 70 50 42 6 2 7 4 6 6

6 75 55 47 4 2 6 3 6 6

7 80 60 52 3 1 4 2 5 4

Sum 49 23 54 30 43 42

Performance

Scenario 2: Slower lead vehicle

Slight+ SV Serious+ SV Fatal SV

46% 55% 97%

Stopped

Slower

Braking

Overall 23% 31% 57%

46% 55% 97%

16% 24% 36%

Slight+ SV Serious+ SV Fatal SV

16% 23% 50%

Stopped

Slower

Braking

Overall 13% 22% 52%

30% 41% 96%

8% 17% 31%

Slight+ TV Serious+ TV Fatal TV

8% 16% 43%


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Finally, we provide the values that are used in the subsequent chapters where the total injury reduction is estimated, i.e. where both the subject and target vehicles are taken into account. Clearly, the total reduction will fall between the reductions for the subject and target vehicles; note, however, that the total reduction is not simply the average of the two individual reductions.

Table 3-8 Overall safety impact assessment for the total injury reduction (taking into account both the subject and the target vehicles)

3.3.1 Separate assessment for autonomous action and driver reaction

For the socio-economic analysis, it is required to compare the safety benefits of pre-crash systems in case of a purely autonomous action with that assuming a driver reaction. The necessary values may be obtained by essentially the same assessment method as that described in Sections 3.3.1 – 3.3.2; the only part that needs to be changed is the section about the human-machine interaction. In fact, by changing the parameter 80% in that section to 0%, one obtains the safety benefit for autonomous action only, while changing the parameter to 100% provides the benefit with driver reaction (assuming that all drivers actually react to the warning, which is not the case today). The results, using the same injury risk and crash frequency functions and test results as above, are summarized in the following tables.

Table 3-9 Overall safety impact assessment for injury reduction with purely autonomous action of the pre-crash system

Table 3-10 Overall safety impact assessment for injury reduction with a pre-crash system assuming a warning system that triggers driver reaction with 100% efficiency

There is a clear difference between the autonomous case and the one with 100% driver reaction especially in the slower lead vehicle scenario. However, no difference can be observed in the stopped lead vehicle scenario, which causes that the overall difference between these two cases is not that large. The reason for this is that in the mean impact speed reductions in the ASSESS tests A3A1 and A3A3 (i.e. without and with driver reaction in the stopped lead vehicle scenario at initial speed 50 km/h) were essentially the same.

Stopped

Slower

Braking

Overall 20% 29% 55%

42% 51% 97%

13% 22% 34%

Slight+ SV+TV Serious+ SV+TV Fatal SV+TV

14% 21% 48%

Stopped

Slower

Braking

Overall 17% 26% 52%

Subject vehicle Target vehicle Subject & target vehicles together

30% 41% 89%

13% 21% 31%

Slight+ Serious+ Fatal

14% 21% 48%

32% 86%

8% 16% 28%

10% 20% 49%20% 28% 54%


8% 16% 43%

19%34% 44% 90%

15% 23% 32%


16% 23% 50%

Stopped

Slower

Braking

Overall 29% 56%

14% 22% 35%

24% 32% 58% 14% 23% 53% 21%

16% 24% 37% 8% 17% 31%

48%

49% 57% 99% 32% 43% 99% 45% 54% 99%

Serious+ Fatal

16% 23% 50% 8% 16% 43% 14% 21%

Subject vehicle Target vehicle Subject & target vehicles together

Slight+ Serious+ Fatal Slight+ Serious+ Fatal Slight+


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4 Socio-economic assessment

4.1 General procedure and assumptions of the socio-economic assessment

An essential aspect of WP2 (Socio economic evaluation and legal aspects) of the ASSESS project is measuring the benefits conferred by the forward-looking safety system. The objective is to provide a thorough assessment of the safety impacts. Therefore, a monetary valuation of the safety benefits has been carried out. However, since a reliable estimation of system cost is currently very difficult a Break-Even Analysis (BEA) is carried out instead of the classical cost-benefit approach in order to calculate critical safety system cost. Pre-crash functionalities use components of other safety systems such as adaptive cruise control and thus a separation of costs is not possible and connected with some uncertainties due to highly confidential cost information of OEMs. Cost data from other projects like eIMPACT are quite old and do not fit very well to the bandwidth of functionalities of current pre-crash systems and their difference in performance. Since the BEA follows the principles of a classical CBA in order to assess the monetary safety benefit of pre-crash systems, the calculated critical safety system costs represent target break-even costs, which indicate the maximum cost of the system that would still provide societal benefits and therefore would comply with a benefit-cost ratio of more than one. Critical safety system costs are defined by equating aggregated safety benefits of vehicles with the system on board and the corresponding fleet costs. Consequently, critical costs can directly provide information about absolute socio-economic efficiency of the pre-crash systems only in terms of target cost of the regarded system. If these target cost are exceeded by the factual cost of a certain system, profitability from an overall society point of view will not be given anymore. However, critical safety system costs can provide information about the relative efficiency of a pre-crash system at different points of time and of relative efficiency of pre-crash system with different functionalities, and thus allowing a comparison and ranking of systems. For example a safety system A which has higher critical system cost than a safety system B can be considered as more efficient. Also a safety system A in situation X which has higher critical system costs than the same system in situation Y can be considered as more efficient in situation X than in situation Y. In this report break-even analysis is used for comparing the effectiveness of systems with different functionality level like purely autonomous action of the pre-crash system vs. a warning system that triggers driver reaction with 100% efficiency. Based on the previous work in D2.1 about the integrated methodology framework (cost-unit rates, definition of time horizon etc.) the following key parameters and assumptions have been taken into account for conducting the BEA and the monetary assessment of the critical safety system costs (see also Dobberstein et al. 2011):

• The assessment of the safety systems will focus on safety impacts measured by prevented and mitigated causalities in EU-27. The cost savings of avoided congestions related to accidents with causalities are also considered in the assessment.

• Direct traffic effects of collision mitigation and avoidance systems due to harmonizing the traffic flow and prevented emissions are not considered. Reasoning behind this that the effects are rather marginal as was shown in previous EU studies such as the projects on vehicle safety systems CODIA (Kulmala et al. 2008) and eIMPACT (Wilmink et al. 2008).

• Time horizon for assessment of the benefits and critical safety system costs are the years 2020 and 2030.

• The benefits of the safety system in use (“with-case”) are compared to the situation where no such safety system is installed in passenger cars (“without-case”).


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• All monetary values are expressed in prices of 2011.

• The safety system produces a stream of impacts and safety benefits over the life time of the vehicle. The average economic lifetime of a vehicle is assumed to be 12 years (Baum et al. 2008). This means that all costs and benefits arise at different points in time. A discount rate of 3% ensures that the stream of benefits and system costs are compared for a common base year (2011).

4.2 Variants of Break-even analysis in ASSESS project

Depending on scope and goal of the assessment three variants of break-even analysis (BEA) can be defined. All variants use ASSESS testing outcomes from other WP. The following illustration shows the different BEA approaches which are addressed in this report:

Figure 4-1: Variants of Break-even analysis based on ASSESS testing (own figure)

• BEA I: Test-based assessment of pre-crash system performance and

effectiveness By testing the system’s performance under realistic conditions, the ASSESS project is able to show evidence that the focused systems are able to avoid or mitigate crashes according to test scenarios based on accident analysis. Thus compared to expert and theoretical based impact estimation the test results provide first empirical evidence for estimation of the safety potential of the systems. Hence, the safety benefits of the systems are evaluated as accurately as possible given available empirical evidence provided by the ASSESS project.

• BEA II: Comparison and ranking of different pre-crash systems based on effectiveness and efficiency estimation Main objective of ASSESS is to provide an assessment tool box which can be used by different manufacturers and institutions to prove their system’s effectiveness and performance. Testing of the systems can lead to identification of performance differences between systems with different functionalities which allows ranking of

not tested safety impacton other conflicts

Test basedsafety impact

BEA III – Theoretic

overall potential

BEA I – Test-

based proven

potential

Test basedsafety impact

Test basedsafety impact of system A

Critical system cost


A

BEA II – Test-

based ranking of

safety systems

Critical system cost B

Test basedsafety impact of system B


Critical system cost ⇒

System benefits = fleet system cost


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systems by their effectiveness and by break-even analysis derived critical system costs. However, the figures for safety impacts of different systems will reflect differences of generic safety systems and not of systems of different OEMs.

• BEA III: Assessment of overall theoretic safety potential of pre-crash systems Following the eIMPACT project, where the safety impacts were derived by experts´ safety impact estimation, the pre-crash systems may avoid and mitigate accident types other than rear-end collisions which are in focus of testing in ASSESS (e.g. single vehicle collisions with obstacles beside road, angle collisions). These additional safety impacts are included in impact estimation, which therefore provides a more complete picture of the overall safety impact of the system . Because test-based safety impact is in the focus of ASSES, this approach is shown in the sensitivity analysis.

4.3 Cost-unit rates

The safety benefits of the systems are expressed in terms of avoided accidents, fatalities, severely injured, and slightly injured. If the accident cannot be avoided, it should at least be possible to reduce the accident severity. This means that a former fatality becomes a severely or even a slightly injured person. The cost-unit rates used in ASSESS have to be valid for EU 27. The project HEATCO (Bickel et al., 2005) aimed at harmonizing the guidelines for project assessment including road safety programs. For the European countries cost-unit rates for road accidents were proposed. However, the HEATCO project does not provide uniform cost-unit rates for EU 27. In the ASSESS project weighted average values of cost-unit rates provided by HEATCO are applied to get a ranking of accident scenarios with fatalities, serious and slight injuries (ASSESS, deliverable D1.1, McCarthy et al. 2010). This approach will also be followed here. For calculation of average cost-unit rates of injury levels a weighting is needed which allows transfer of money value between different countries. Therefore, as was proposed by the HEATCO project we use for these value transfer Gross Domestic Product (GDP) per capita for weighting of the cost-unit rates (Bickel et al., 2006). Of course, the cost-unit rates of some member states are determined by consumer surveys using the willingness-to-pay method, whereas other countries use the cost-of-damage approach for valuing casualties. If one follows the cost-of-damage approach for valuing casualties, cost-unit rates for fatalities are determined largely by losses of productivity. To a lesser degree this holds also for serious and slight injuries. But working productivity has a strong impact on the income level of society, and thus on resulting GDP. If one follows the willingness-to-pay approach the determined value for avoiding risk of fatality also depends strongly on the income level of society. The income level reflects economic well-being of society, and usually richer countries value fatality and injury risk reduction more than poorer countries. Therefore, for inter-country comparison of cost-unit rates these values have to be corrected for differences in economic wealth of society. GDP per capita gives some indication on this. The table below gives an overview of cost-unit rates for the year 2005 based on HEATCO (Bickel et al. 2005). The eIMPACT project has provided an update of the values from 2002 to 2005. For comparison and averaging, the values are weighted by GDP per capita. The table entails thus the weighted country specific cost-unit rates, and the weighted average cost-unit rates.


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Table 4-1: Casualty costs-unit rates in € for 2005 in EU 25 in factor prices (Assing et al. 2006)

*Purchasing Power Parity ** For weighting only those countries are included for which for all injury level values are available.

In the D2.1 about the methodology framework of the socio-economic assessment it was decided to include congestion cost per accident in the assessment. Therefore, per fatality congestion costs of 15,000€, and per injury congestion costs of 5,500€ were chosen (€-2003 price level, see also Assing et al. 2008). Property-damage only accidents are outside the scope of assessment objectives of the ASSESS project, since reliable European figures for rear-end accidents without injuries are not available. Because congestion costs are given per fatal accident but numbers on fatal accidents are not directly available a weighting factor is applied to the number of fatalities for getting the number of fatal accidents. The weighting factor is calculated by use of the European average of the share of accidents to fatalities (≈0.90).

Because the assessment is done for benefits arising in the period between 2020 and 2030 cost-unit rates based on causality costs have to reflect the change in productivity per capita for valuing the human resource losses. We will follow this proposal by using forecasted growth rates of GDP per capita for EU-27 by using an annual real growth rate of 2 % (EC 2008).

Table 4-2 shows the cost-unit rates for personal damage and congestion costs related to accidents, scaled up from year 2003 and 2005 values to year 2010, 2020 and 2030.

Table 4-2: Cost-unit rates for casualties and injury accidents (€-2011)

Country FatalitySerious

Injury

Slight

Injury

GDP per

capita in

PPP* (EU-27

= 100)

(2005)

Fatality -

weighted**

Serious

injury -

weighted**

Slightly

injury -

weighted**

North/West Denmark 692.143 71.546 19.528 124 558.180 57.698 15.748

Finland 1.752.000 365.000 44.300 114 1.536.842 320.175 38.860

France 1.362.770 204.416 29.981 111 1.227.721 184.159 27.010

Germany 1.199.780 83.454 3.652 117 1.025.453 71.328 3.121

Sweden 1.364.503 243.430 13.637 122 1.118.445 199.533 11.178

UK 1.565.720 175.940 13.567 122 1.283.377 144.213 11.120

East Hungary 896.981 62.239 8.238 63 1.423.779 98.792 13.076

Latvia 709.636 16.149 191 49 1.448.237 32.957 390

Slovak Republic 221.530 39.344 704 60 369.217 65.573 1.173

South Portugal 355.483 16.663 1.111 79 449.978 21.092 1.406

Italy 485.477 105 462.359

Spain 227.547 102 223.085

902.798 127.818 13.491 97 1.044.123 119.552 12.308Average

€-2011 2005 2010 2020 2030

Fatality 1,044,000 1,119,200 1,364,298 1,663,072

Serious

injuries 119,000 127,572 155,509 189,565

Slight

injuries 12,000 12,864 15,682 19,116

Fatal

accident 15,159 16,008 19,513 23,787

Injury

accident 4,075 4,303 5,246 6,394

Causality

costs

Congestion

costs


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By the use of cost-unit rates weighting factors for accident seriousness, which were calculated in WP1 of ASSESS (ASSESS D1.1, McCarthy et al. 2010), frequencies of accident types were ranked. The cost-unit rate for fatalities as basis weighting factor was defined as 1.0, then the weighting factor for serious injuries was calculated as 0.11 and for slight injuries as 0.011. By use of casualty costs such as defined above the same factors result.

4.4 Vehicle cost and vehicle fleet

It was pointed out earlier that it has not proved possible during the project duration to generate accurate and reliable estimates of the costs of the considered pre-crash systems. Because pre-crash functionalities use components of other safety systems such as adaptive cruise control and thus a separation of costs is not possible and connected with some uncertainties due to highly confidential cost information of OEMs, experts within the consortium stressed the fact that it is currently not possible to provide information on the manufacturing cost of the systems. Cost data from other projects like eIMPACT are quite old and do not fit very well to the bandwidth of functionalities of current pre-crash systems and their difference in performance. The following table summarizes estimated cost prices9 for components and safety systems of the eIMPACT and SAFESPOT project. The eIMPACT cost values are based on a workshop with participants from CRF, DAI, Renault, Volvo, Bosch and on literature analysis.

Table 4-3: Overview on cost price data provided by eIMPACT (Baum et al. 2008), SAFESPOT 2010 (Schindhelm et al. 2009)

Component 2010

(cost prices) 2020

(cost prices)

Long-range radar front (LRR) 100€ 100€

[84€ (SAFESPOT)]

Short-range radar (24 GHz radar network) 50€ 40€

Mono camera (front) (NIR,FIR) 75€ 60€

Warning Module (visual, acoustic) 7€

[20€ (SAFESPOT)] 5€

[10€ (SAFESPOT)]

Haptic steering wheel 15€ 10€

Emergency braking system1, 2, 3

Cost price

• Price range: 550€ - 2,000€ (2005)

• Cost price range: 180€ - 650€ (2005) 100€ – 400€ 100€ – 200€

1with ESP as subfunction, 2same costs as estimated for ACC, 3included are LRR and Warning module

The displayed values are net system cost of intelligent vehicle safety systems (IVSS) which are defined as marginal manufacturer cost that include all resources (R&D, production marketing) necessary to equip the fleet with a system and income of manufacturers and suppliers for the market entry or establishing a market for these technologies. It has to be pointed out that the shown values are not the system costs for the customers since market prices are much higher due to taxes and profits of the OEMs. In the socio-economic approach within the EU project eIMPACT the term of the cost price was introduced which comprises the price of the ICT system paid by the manufacturer to its supplier plus a mark-up for in-vehicle implementation (Assing et al. 2006). Generally, in the face of limited evidence it is useful to apply the “Factor 3” rule of thumb, which means that in the automotive industry market prices for ICT systems differ from the cost prices by a factor of 3 (Malone et al. 2008).

9 The price the OEM pays to the safety system or component supplier is called Cost price.


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Therefore, due to the lack of reliable cost information the approach to estimate the socio-economic benefits was changed form a classical cost-benefit analysis to a Break-Even Analysis (BEA) in order to calculate critical safety system cost. This approach is in line with former projects which also had no reliable cost information of pre-crash systems (Grover et al. 2008). Since the BEA follows the principles of a classical CBA in order to assess the monetary safety benefit of pre-crash systems, the calculated critical safety system costs represent target break-even costs, which indicate the maximum cost of the system that would still provide societal benefits and therefore would comply with a benefit-cost ratio of more than one. Subsequently, these target costs represent the “cost price” (manufacturing costs) and therefore have to be multiplied with the factor three to get an indication of the target market prices for the customers. Nevertheless, in order to calculate the critical safety system costs in the BEA the annual benefits of the regarded systems have to be confronted with the respective vehicle fleet equipment rate. Because the socio-economic benefit assessment will be done for the target years 2020 and 2030, the critical safety system costs have to be annualised. This means that the costs are distributed over the lifetime of the vehicle – it is assumed that the lifetime of the safety system equals the lifetime of the passenger car. The average economic lifetime of a vehicle in EU-25 is about 12 years (Baum et al. 2008). Subsequently, the costs for the safety system have to be multiplied with the annuity rate to get the yearly average (annual) costs. The annuity rate is determined by the following formula:

1004601031

031030

11

112

12

..

).(*.

)d(

)d(*dAR

T

T

=

−

=

−+

+= , with

AR annuity rate

d discount rate (3 %)

T service time (12 years). The social discount rate of 3% (real), which is used here, is in line with HEATCO guidelines on road safety project assessment (Bickel. et al. 2006). This discount rate was also used in the CODIA, eIMPACT and the SAFESPOT projects on vehicle safety systems.

To define the complete costs for the equipped vehicles, the system costs for the assumed safety system have to be multiplied with the annuity rate (AR=0.10046), the penetration rate and with the estimated vehicle stock of passenger cars. In general this approach is displayed in the following formula:

Sttt CFPVSC ⋅⋅= , with

Ct Estimated value of total fleet costs per year t [EUR].

t Considered year of assessment period 2020 to 2030

VSt Vehicle stock in year t

FPt Fleet penetration rate in year t [%]

CS Annualised costs per system (cost per system*AR)

Using the vehicle stock data for the target years and the respective market penetration rates from chapter 2 (see Table 2-6, Table 2-7) the complete cost formula can be filled with the considered numbers in order to generate the results for the mean scenario:


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Mio 264,195*0.05*0.10064*cost ystem(2020) costs annual Total 2020S=

Mio 288.498*0.15*0.10064*cost ystem(2030) costs annual Total 2030S=

To define the complete costs for the equipped vehicles, the system costs per unit for the assumed safety system have to be multiplied with the annuity rate (AR=0.10046), the penetration rate (mean scenario: 2020: 5%; 2030: 15%) and with the estimated vehicle stock of passenger cars in the year 2020 and 2030.

4.5 Break-even analysis (test based) – BEA I and BEA II

4.5.1 Benefit assessment for mean scenario

Based on the results of the safety impact assessment, the reduction potential of accidents, injuries and fatalities is determined according to the safety impact assessment methodology described in section 3.3. In this test based safety impact assessment the safety impact and the corresponding benefits are limited to the test-results of the described scenarios which were tested. Since rear-end test scenario A is in focus of ASSESS the impact assessment is limited to rear-end collisions on EU-27 level. The benefit estimation is conducted for three different system variants (autonomous action, warning system with 100% triggering of driver reaction, combination of both). Taking the overall safety impact for subject and target vehicle into account the number of reduced casualties are calculated via multiplying the safety benefit in terms of a percentage change of injuries and fatalities of rear-end crashes (see Table 4-4) with the EU-27 target group numbers (see Figure 2-5) for the mean accident data forecast (optimistic and pessimistic scenario are part of the sensitivity analysis). Furthermore, it is assumed that the mitigation effect as well as the market penetration rate (mean scenario: 5% in 2020, 15% in 2030) are equal for the three considered country clusters. Consequently, the accident and casualty numbers of the target group for EU-27 have to be adjusted by the share of the corresponding fleet respectively the vehicle mileage (see Table 2-7). Finally, this leads to the reduced total number of casualties and accidents for the years 2020 and 2030 as shown in Table 4-5.

Table 4-4: Percentage reduction of fatalities and injuries (own calculations)

System Variants of

pre-crash system

Impact on

fatalities

Impact on

serious injuries

Impact on

slight

injuries

Purely autonomous

action 52% 26% 17%

Warning with 100%

triggering of driver

reaction 56% 29% 21%

Combination of

warning and

autonomous action 55% 29% 20%


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Table 4-5: Safety impact in terms of avoided accidents and casualties for the mean accident data forecast and mean penetration rate (own calculations)

The safety impact ranges between 31 and 34 for fatalities, 217 and 242 for serious injuries and 1,611 and 1,990 for slight injuries in 2020 on EU-27 level depending on the considered system variant. Due to the higher share of equipped vehicles the reduction effect is overall higher in 2030. The number of avoidable accidents with a given severity equals or is somewhat lower than the corresponding number of casualties because in one accident multiple occupants can be involved. For calculation of the socio-economic benefit the total impact numbers in terms of reduced accidents and casualties have to be monetised. Using the monetary values displayed in Table 4-2 lead to accident cost savings of nearly 110 million Euro for the purely autonomous function and of 124 million Euro for the warning system in 2020 respectively. Due to the higher penetration rate of the systems in 2030 the safety benefit increases and ranges between nearly 244 and 278 million Euro. Table 4-6 gives an overview of the safety benefits for 2020 and 2030 concerning the various system variants.

Table 4-6: Safety benefit in Euro for 2020 and 2030 for the mean accident data forecast and mean penetration rate (own calculations)

4.5.2 Critical safety system costs for mean scenario

In a final step, the estimated benefits can be used for the calculation of the critical safety system costs. In order to give an indication of the overall economic profitability of a certain system, the safety benefits must be at least as high as the total fleet costs of the system. In a classical cost-benefit analysis this would mean a benefit-cost ratio of 1 or higher.

2020

System Variants of

pre-crash system

Fatal


Serious

injuries

Slight

injuriesPurely autonomous

action 31 1,367 31 217 1,611

Warning with 100%


reaction 34 1,663 34 242 1,990

Combination of

warning and

autonomous action 33 1,595 33 242 1,895

2030

System Variants of

pre-crash system

Fatal


Serious

injuries

Slight

injuries

Purely autonomous

action 51 2,717 51 415 3,223

Warning with 100%


reaction 55 3,306 55 462 3,981

Combination of

warning and

autonomous action 54 3,171 54 462 3,791

System Variants of

pre-crash system

Safety Benefit in

2020 (in 1,000 €)

Safety Benefit in

2030 (in 1,000 €)

Purely autonomous

action 109,288 243,946

Warning with 100%


reaction 123,992 277,923

Combination of

warning and

autonomous action 121,321 271,772


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Via rearranging the following equation the critical safety system costs can be calculated using the safety benefit and fleet penetration data:

fleet car*rate npenetratio fleet*cost system annualbenefitsSafety = .

fleet Car*rate npenetratio Fleet*rateAnnuity

benefitsSafety cost systemsafety Critical

fleet Car*rate npenetratio Fleet*rateAnnuity *cost ystembenefitsSafety

=⇔

=⇔ S

The calculated critical system costs show depending on the system variants a range of system costs of 82 € to 93 € in the assessment period 2020, and of 56 € to 64 € in the assessment period 2030. These critical system safety costs define target break-even costs, which indicate the maximum cost of the system that would still provide societal benefits. If systems with these or lower costs would come to the market in 2020 and 2030 then socio-economic efficiency of the systems would result (Benefit-cost ratio equal or larger then 1).

Table 4-7: Critical safety system costs for the mean accident data forecast and mean penetration rate (own calculations) (€-2011)

Since the benefits do not increase in the same magnitude than the fleet equipment rate from 2020 to 2030 the critical safety system costs decrease. Taking the above mentioned “Factor 3” rule of thumb into account market prices for the pre-crash systems could be in the range of 247 € to 280 € in 2020 and of 168 € to 192 € in 2030 and still be efficient from an overall society point of view. Finally, the results can be used to rank the effectiveness and efficiency of the different system variants. Compared to the purely autonomous function the warning system with 100% driver reaction triggering is nearly 13-14% more efficient from an economic point of view.

4.6 Sensitivity analysis

This chapter intends to provide a broader picture and interpretation of the break-even results as displayed in chapter 4.5. The sensitivity of results is addressed at three different levels, starting from parameter changes of the calculation model (market penetration, accident data forecast), then taking a full equipment scenario for 2020, and 2030 into account and finally, enlarging the model to incorporate further safety impacts due to other avoided accidents than only rear-end collisions. It becomes obvious that at later stages of this chapter, only references to the nature of potential impacts can be provided and the statements do not longer base on findings and measurements from the testing itself. It should be noted that these sections are embedding ASSESS in a more general and broader context.

Purely autonomous

action 82 € 56 €

Warning with 100%


reaction 93 € 64 €

Combination of

warning and

autonomous action 91 € 63 €

Critical safety

system cost in

2020 (in €)

System Variants of

pre-crash system

Critical safety

system cost in

2030 (in €)


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4.6.1 Scenario analysis for accident data and market penetration forecast

The first sensitivity test tries to cover the uncertainty of accident data and market penetration forecasts. Sensitivity analysis in general means to test the stability of assessment results by variation of input parameters based on literature or theoretical reasoning. Thus it is proven whether these parameters can be lowered or increased without strong impact on the assessment results, such that results can be considered as stable. The sensitivity analysis is conducted in the following way for four forecasted and therefore uncertain parameters:

• Market penetration scenarios: minimal fleet penetration scenario: 2020: 3%; 2030: 10% vs. maximal fleet penetration scenario: 2020: 10%; 2030: 30%, full penetration in 2020 and 2030

• Accident data forecast: optimistic vs. pessimistic accident trend

• Combination of both parameter changes Market penetration Variation of fleet penetration rate has an impact on the side of safety benefits and the cost side of the BEA. On the cost side from production volume independent and therefore constant costs per safety system are assumed. Thus the total fleet cost increase linear with fleet penetration rate. Critical factor for safety benefits is the share of mileage driven by the equipped passenger car. As was shown above in section 2.4 vehicle age has an impact on that. Because of that it was shown that the share of mileage driven by equipped cars is higher than the penetration rate of these cars. For the relatively low penetration rates considered here, the difference between fleet penetration rate and share of mileage driven increases absolutely if the penetration rate gets larger and vice versa. Nevertheless, doubling of the penetration rate leads to a disproportional increase of the driven mileage since the share of older cars equipped with the system increases. For the sensitivity analysis the fleet penetration parameter is changed to a lower (2020: 3%, 2030: 10%) and to a higher rate (2020: 10%; 2030: 30%) as well as a full penetration scenario. Accident trend By lowering or increasing the accident and casualty level, in general, uncertainty about forecasting the accident trend is taken into account. For the safety impact assessment the mean accident scenario was assumed. Thus, in sensitivity analysis the optimistic and the pessimistic accident trend scenario are applied. Thus a “higher” accident trend prevails if the pessimistic trend is assumed and vice versa. BEA-Results of sensitivity analysis Table 4-8 shows the results of the sensitivity analysis given the parameter changes. Interpretation of the results is straightforward. Given the mean penetration rate an optimistic accident trend decreases the total benefits and the critical safety system costs of the combination variant of the pre-crash system and vice versa. The reasoning behind this is that the assumed positive trend in road safety is assumed to continue in EU-27. This will result in a decreasing number of accidents and casualties and therefore the forecasted accident target population in 2030 will be smaller than in 2020. In comparison a lower penetration rate of equipped vehicles leads to overall lower benefits but to higher critical safety system costs because of the relatively higher mileage share of the low number of equipped new vehicles. Since the benefits do not increase in the same magnitude than the fleet equipment rate from 2020 to 2030 the critical safety system costs decrease in every observed case. Overall, the critical safety systems cost range between 46 and 121 Euro in 2020 and between 34 and 88 Euro in 2030. In sum, the sensitivity analysis shows the expected results.


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The assessment model can be considered as stable with regard to variation of important parameters.

Table 4-8: Results of sensitivity analysis (market penetration, accident trend) (own calculations)

4.6.2 Extended Break-even analysis (BEA III)

This socio-economic assessment approach enlarges the before shown test-based assessment with safety impact assessment results of the eIMPACT project for an emergency braking (EBR) system. The EBR system assumed in eIMPACT includes a warning and autonomous emergency braking function. In eIMPACT, in addition to rear-end collisions further accident types were part of the impact assessment of EBR system. The safety impact estimation mainly was based on theoretical considerations and expert estimation. The BEA shown here combines test-based safety impact assessment results from ASSESS for the rear-end collision type with safety impact assessment results available for other accident types from the eIMPACT project. Thus, the safety impact results presented here can be interpreted as an indication of overall safety potential of the EBR system. The benefit estimation is conducted for the combined system variant (warning + autonomous braking). Taking the overall safety impact for subject and target vehicle into account the number of reduced casualties is calculated via multiplying the safety benefit in terms of a percentage change of injuries and fatalities of rear-end crashes with the EU-27 target group numbers for the mean accident data and market penetration rate forecast. Since the analysis in eIMPACT was based on an older pre-crash system it is assumed that the effectiveness of this system amounts to 80% of the functionality of the up-to-date systems tested in ASSESS. Beside rear-end collisions the following other accident types were included in the impact assessment in eIMPACT (Wilmink et al. 2008):

• “Collision on the road with all other obstacles” (single vehicle accident) (Type 2)

• “Collision beside the road with obstacle” (single vehicle accidents) (Type 3)

• “Angle collision” (two vehicle accidents) (Type 6)

• “Other accidents with two vehicles” (Type 8) The overall target population for 2020 and 2030 from which safety benefits may be derived is described below by using results of the eIMPACT project on socio-economic assessment of the EBR system. Accident target population of eIMPACT includes in addition to passenger cars, further vehicle types like buses, coaches, and trucks with weight above 3,5t. But, it is possible to analyse the data base separately for light-vehicle accidents. Therefore, only light-to-light vehicle accidents for the three clusters from the TRACE/eIMPACT accident data are aggregated and extrapolated to 2020 and 2030 to follow the ASSESS accidentology.

Base

Full

penetration

scenario

mean scenario

(accident data/market

penetration)

optimistic

scenario

pessimistic

scenario lower higher

pessimistic/

lower scenario

optimistic/

higher scenario

mean accident

trend

Benefits

(in 1,000 €) 121,321 99,446 143,196 81,826 207,029 96,580 169,700 1,212,188

Critical safety

system costs (in €) 91 € 75 € 108 € 103 € 78 € 121 € 64 € 46 €

Benefits

(in 1,000 €) 271,772 179,984 363,561 198,810 463,766 265,957 307,133 1,164,067

Critical safety

system costs (in €) 63 € 41 € 84 € 66 € 51 € 88 € 34 € 40 €

2020

Year

2030

Accident trend Market deployment

Combination of accident

data and market penetration

variation


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The following table shows the accident target population based on the accident data forecast of the eIMPACT project (Wilmink et al. 2008) for rear-end collisions and the other mentioned additional accident types:

Table 4-9: Overall target population for all accident types affected by emergency braking system (EBR), EU-27, 2020, 2030

(own calculation, based on Wilmink et al. 2008)

The percentage effectiveness of the emergency braking (EBR) system on injury accidents and casualties which were estimated in the eIMPACT project on accidents are given below. These four accident types will not be covered by the ASSESS scenarios or are not yet feasible to be tested (e.g. junction collision). Maximal system effectiveness (100 %) according to eIMPACT estimation of safety impact is assumed.

Table 4-10: Percentage reduction of fatalities and injuries, other accident types than rear-end, 100 %-performance of EBR-system

(own calculation, based on Wilmink. et al. 2008)

2020

Fatal

accidents

Injury

accidents Fatalities

Serious

injuries

Slight

injuries

Rear-end

collision 599 76,461 599 8,328 94,682

Other

obstacles 1,324 36,544 1,324 9,298 40,112

Obstacles

beside road 4,189 70,531 4,189 25,464 75,434

Angle collision 2,407 154,263 2,407 33,211 178,558

Other

accidents 510 32,113 510 6,214 35,630

Sum 9,029 369,913 9,029 82,515 424,418

2030

Fatal

accidents

Injury


Serious

injuries

Slight

injuries

Rear-end

collision 422 59,549 422 6,829 81,200

Other

obstacles 513 16,509 513 4,410 20,494

Obstacles

beside road 1,622 31,229 1,622 12,078 38,541

Angle collision 932 70,919 932 15,753 91,229

Other

accidents 197 14,760 197 2,947 18,204

Sum 3,686 192,966 3,686 42,018 249,669

Fatal

accidents

Injury


Serious

injuries

Slight

injuries

Other

obstacles -24% -20% -24% -20% -20%

Obstacles

beside road -12% -10% -12% -10% -10%

Angle collision -6% -5% -6% -5% -5%Other

accidents -12% -10% -12% -10% -10%


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By enlarging the safety impact assessment based on the ASSESS mitigation model with safety impact estimation based on eIMPACT for other than rear-end collision types a theoretical accident reduction potential results. This educated guess is used here for overall impact assessment of the EBR-system. Since only these accidents are affected where vehicles with systems equipped are involved, the fleet and traffic penetration rates of the mean scenario are used to estimate the expected safety impact in 2020 and 2030 (see Table 2-7). Thus the following accident and casualty reduction as well as benefits and critical safety system costs result for the enlarged break-even approach:

Table 4-11: Benefit impact and critical system costs for the enlarged BEA III scenario

The enlarged BEA shows that due to the inclusion of other collision types the benefits of the considered pre-crash system increase considerably for the years 2020 (256 €) and 2030 (119 €). Since the benefits do not increase in the same magnitude than the fleet equipment rate from 2020 to 2030 the critical safety system costs decrease. This is influenced amongst other things by the accident trend decline which lowers the target population of the pre-crash system in 2030. Taking the above mentioned “Factor 3” rule of thumb into account market prices for the pre-crash systems could be then in the range of approximately 770 € in 2020 and of 360 € in 2030 and still be efficient from an overall society point of view. Compared to actual market prices of adaptive cruise control in combination with a forward collision warning system with autonomous braking function under 30 km/h of an Audi A3 car (560 €) and taking economies of scale into account the tested pre-crash system seems highly profitable in 2020 if the safety impact includes more than rear-end accidents.

Avoided fatal accidents 109 118

Avoided injury accidents 3,301 4,674

Avoided fatalities 109 118

Avoided serious injuries 729 962

Avoided slight injuries 3,763 5,711

Benefits (in 1000 €) 339,861 519,760

Critical safety system costs (in €) 256.10 119.56

2020 2030Benefit impacts BEA III


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5 Conclusions

The main results of the socio-economic assessment can be summarized as follows:

• In the first part of the report background data which are needed for socio-economic assessment are provided. This includes the forecast of fatality figures for the assessment period 2020 and 2030, and a forecast on the passenger car fleet based on ProgTrans data. The estimated fatality trend shows (medium scenario) between 2010 to 2020 a reduction of nearly 35 %, and for the next decade (2020 – 2030) a further fatality reduction of about 9 %. For the European car fleet a growth rate of about 10% between 2010 and 2020 and of about 9% between 2020 and 2030 was estimated.

• Because safety benefits of the system depend on the participation of the equipped cars in traffic, the share of equipped cars was transferred to the share of mileage driven by equipped cars. Since age is a strong predicator of annual car mileage, by using German car market data a linear regression analysis of a functional relationship between (age-dependent) share of car fleet and corresponding (age-dependent) share of mileage driven was specified.

• The core of the safety assessment is a mitigation model from WP1 based on the concept of injury risk curves formalising the relationship between fatality / injury risk and delta-v. In the model a lognormal distribution of accidents over impact speed parameter delta-v is used following US-accident data because similar data at this level of detail are missing for Europe. This accident distribution is used as input for estimation of an impact of the pre-crash system on the accident distribution function. Combined with injury-risk functions then percentage reductions of casualties are predicted. These changes are interpreted as system effectiveness with respect to different reductions of delta-v caused by the pre-crash system. Based on the testing results the mean safety impact of the pre-crash system accounts for an decrease of nearly 55% of fatalities, 29% serious injuries and 20% slight injuries in rear-end accidents.

• Since a reliable estimation of system cost is currently very difficult a Break-Even Analysis (BEA) is carried out instead of the classical cost-benefit approach in order to calculate critical safety system cost. Pre-crash functionalities use components of other safety systems such as adaptive cruise control and thus a separation of costs is not possible and connected with some uncertainties due to highly confidential cost information of OEMs. Cost data from other projects like eIMPACT are quite old and do not fit very well to the bandwidth of functionalities of current pre-crash systems and their difference in performance. Since the BEA follows the principles of a classical CBA in order to assess the monetary safety benefit of pre-crash systems, the calculated critical safety system costs represent target break-even costs, which indicate the maximum cost of the system that would still provide societal benefits and therefore would comply with a benefit-cost ratio of more than one.

• For the socio-economic assessment generic pre-crash system variants including warning and autonomous emergency braking were assumed. Based on the results from the safety impact analysis a range of critical safety system costs have been calculated. For the mean scenario with an assumed market penetration of the car fleet with pre-crash systems of 5% in 2020 and 15% in 2030, a reduction of fatality figures ranging from 31 to 34 (2020) and from 51 to 54 (2030) was calculated for the different system variants. The maximum total safety benefit in the mean scenario accounts for nearly 124 million Euro in 2020 and 278 million Euro in 2030 respectively. This results in critical safety system costs of 93 (2020) and 64 (2030) Euro which represent manufacturing cost of the system. Taking the well established “Factor 3” rule of thumb into account market prices for the pre-crash systems could


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be in the range of 280 (2020) and 192 (2030) Euro and still be efficient from an overall society point of view.

• The sensitivity of results is addressed at three different levels, starting from parameter changes of the calculation model (market penetration, accident data forecast), then taking a full equipment scenario for 2020 and 2030 into account and finally, enlarging the model to incorporate further safety impacts due to other avoided accidents than only rear-end collisions. For changes of the accident trend and market penetration forecasts the calculated critical safety system costs show depending on the parameter changes a range of46-121 Euro in 2020 and 34-88 Euro in 2030. Enlargement of the safety benefit assessment with findings from the eIMPACT project show a considerably decrease in total benefits and target break even cost. Critical safety cost amount to 256 Euro in 2020 due to systems effectiveness on other accident types than only rear-end crashes.

• The applied Break-even analysis methodology has proven its applicability to this type of research question. Up-scaling from micro level (testing) to macro level (EU-27 databases for accidents etc.) provides still considerable challenges, especially concerning the granularity of information. Socio-economic assessment makes typically use of averages of variables whereas distributions of variables would be valuable to keep the value added of testing data. Research in this direction would help to solidify the derivation of socio-economic impacts from testing data. Nevertheless, it is demonstrated how the safety assessment tool and the calculated critical system costs can be used to rank pre-crash systems of different functionalities (warning system vs. purely autonomous pre-crash system etc.).


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6 Risk Register

Risk No. What is the risk Level of

risk10 Solutions to overcome the risk

[WP2R1] Uncertainty about future accident

trend and market penetration of pre crash systems for EU-27 (section 2).

2 Application of three different

scenarios (pessimistic, mean, optimistic) for the socio-economic

assessment in order to give a broader picture of results

[WP2R2] Use of injury-risk curves based on GIDAS-accident data (Hannawald 2008) – Not valid for complete EU-27

2 Review and analysis of available

injury-risk curves

10 Risk level: 1 = high risk, 2 = medium risk, 3 = Low risk


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7 References

ACEA (2010): European Automobile Manufacturers Association, The Automobile Pocket Guide, Brussels.

Aoki, H. et al. (2010): Safety Impact Methodology (SIM) for Effectiveness Estimation of a Pre-Collision System (PCS) by Utilizing Driving Simulator Test and EDR Data Analysis, SAE Technical Paper, doi:10.4271/2010-01-1003.

Assing, K. et al. (2006): Deliverable D3, eIMPACT Project: Socio-economic Impact Assessment of Stand-alone and Co-operative Intelligent Vehicle Safety Systems (IVSS) in Europe, Bergisch Gladbach.

Baum, H. et al. (2008): Cost-Benefit Analyses for standalone and co-operative Intelligent Vehicle Safety Systems, Deliverable D6, eIMPACT Project: Socio-economic Impact Assessment of Stand-alone and Co-operative Intelligent Vehicle Safety Systems (IVSS) in Europe, Delft

Bickel, P. et al. (2005): Developing Harmonised European Approaches for Transport Costing and Project Assessment – HEATCO. Deliverable 2: State-of-the-art in project assessment, Stuttgart.

Bickel, P. et al. (2006): Developing Harmonised European Approaches for Transport Costing and Project Assessment – HEATCO. Deliverable 5: Proposal for Harmonised Guidelines, Stuttgart.

Busch, S. (2005): Entwicklung einer Bewertungsmethodik zur Prognose des Sicherheitsgewinns ausgewählter Fahrerassistenzsysteme, Wolfsburg.

Christidis, P. et al. 2003, Dynamics of the introduction of new passenger car technologies - the IPTS Transport technologies model, Report EUR 20762 EN, Joint research centre European Commission (JRC).

Dobberstein, J. et al. (2010): ASSESS deliverable D2.1, Integrated methodology framework, Cologne.

EC – European Commission (2003): European Road Safety Action Programme, Halving the number of road accident victims in the European Union by 2010: A shared responsibility, Communication from the Commission, COM(2003) 311 final, Brussels, 2.6.2003.

EC – European Commission (2008): European Commision, Directorate-General for Energy and Transport, European Energy and Transport – Trends to 2030 - Update 2007, Brussles.

EC – European Commission (2010): Towards a European road safety area: policy orientations on road safety 2011-2020, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee of the Regions, COM(2010) 389 final, Brussels, 20.7.2010.

EC – European Commission (2011): White Paper – Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system, COM(2011) 144 final, Brussels, 28.3.2011.

Eurostat (2011): URL: http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init= 1&language=de&pcode=tsdtr420&plugin=1.


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Fagerlind, H. et al. (2012): ASSESS deliverable D1.4, Report presenting test methodologies and verification activities of the selected integrated safety systems, Gothenburg.

Gabauer, D., J. and Gabler, H., C., (2006): Correlating Delta-V to Occupant Injury using Event Data Recorders, Proceedings of the American Society of Biomechanics 2006 Annual Meeting, Blacksburg, VA.

Grover, C. et al. (2008): Automated Emergency Braking Systems: Technical requirements, costs and benefits, Published project report PPR 227, Berkshire.

Hampton, C., E., and Gabler H., C., (2009): Influence of the Missing Vehicle and CDC only Delta-V Reconstruction Algorithms for Prediction of Occupant Injury Risk, Biomedical Sciences Instrumentation 45, pp. 238-243.

Hannawald, L. (2008): Multivariate Bewertung zukünftiger Fahrzeugsicherheit: Entwicklung eines Modells zur Bewertung zukünftiger Fahrzeugsicherheit unter Berücksichtigung der Wechselwirkungen von Sicherheitssystemen, Dresden.

Kononen, D.W. et al. (2010): Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes, Accident Analysis and Prevention 43, pp. 112-122.

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Kusano, K. and Gabler, H., C. (2010): Potential Occupant Injury Reduction in Pre-Crash System Equipped Vehicles in the Striking Vehicle of Rear-end Crashes, Annals of advances in automotive medicine / Annual Scientific Conference, Association for the Advancement of Automotive Medicine. Association for the Advancement of Automotive Medicine, Scientific Conference 2010, 54, pp. 203-214.

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Richards, D. and Cuerden, R. (2009): The relationship between speed and car driver injury severity, Transport Research Laboratory, Department for Transport: London, RSWP9.

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Schindhelm, R et al. (2009): SAFESPOT, Cooperative Systems for Road Safety, SP6 – BLADE – SP Business models, Legal Aspects and Deployment, WP6.5 Report on socio-economic, market and financial assessment, Deliverable 6.5.1, Bergisch Gladbach.

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Wilmink, I. et al. (2008): eIMPACT, Socio-economic Impact Assessment of Stand-alone and Co-operative Intelligent Vehicle Safety Systems (IVSS) in Europe, Deliverable D4, Impact assessment of Intelligent Vehicle Safety Systems, Delft 2008.

Wisch, M. et al. (2010): ASSESS Deliverable D1.2, Specifications for scenario definitions, Bergisch Gladbach.

Wrambourg, P. (2005): A new approach to a safe and sustainable road structure and street design for urban areas, Proceedings of the Road Safety on Four Continents Conference, Warsaw, Poland, Reference cited in: Richards, D. and Cuerden, R., The relationship between speed and car driver injury severity, Transport Research Laboratory, Department for Transport: London, RSWP9, 2009.

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