Deliverable D6.1

Technology Opportunities and Strategies towards Climate friendly trAnsport

FP7-TPT-2008-RTD-1

Coordination and Support Action (Supporting)

Deliverable D6.1 (WP 5 report)

Transport Infrastructure Capacity Assessment

(Intelligent Transport Systems and the Impact on Capacity)

ROAD PASSENGER TRANSPORT

National Technical University of Athens

Voula Psaraki-Kalouptsidi, Ioanna Pagoni

Dissemination level

Public PU X

Restricted to other programme participants (including Commission Services) PP

Restricted to a group specified by the consortium (including the Commission

Services)

PE

Confidential, only for members of the consortium (including the

Commission Services)

CO

Deliverable D6.1 i

Coordinator: Dr. Andreas Schäfer

University of Cambridge

Martin Centre for Architectural and Urban Studies, and

Institute for Aviation and the Environment

1-5 Scroope Terrace, Cambridge CB2 1PX, UK

Tel.: +44-1223-760-129

Fax: +49-341-2434-133

E-Mail: as601@cam.ac.uk

Internet: www.toscaproject.org

Contact: National Technical University of Athens

School of Civil Engineering

Department of Transportation Planning and Engineering

5 Iroon Politechniou Str, Zografou Campus

15773, Athens

Tel.: +30210-7721740

Fax: +30210-7724181

E-Mail: dalat@central.ntua.gr

Internet: www.ntua.gr

Voula Psaraki-Kalouptsidi

Tel.: +30210-7721204

Fax: +302107722404

E-Mail: vpsaraki@civil.ntua.gr

Ioanna Pagoni

Tel.: +30210-7721204

Fax: +302107722404

E-Mail: ipagoni@central.ntua.gr

Date: 14.3.2011

Deliverable D6.1 ii

Contents

Contents............................................................................................................................. ii

Abbreviations .................................................................................................................... iii

Abstract..............................................................................................................................1

1 Introduction ...............................................................................................................2

2 Reference System Characteristics ................................................................................3

3 Technology Developments ..........................................................................................5

3.1 Driver Assistance Systems (DAS) ................................................................................. 5

3.2 Automated Highway System (AHS) ............................................................................. 7

4 Constraints on reducing GHG emissions.......................................................................9

4.1 Technological feasibility .............................................................................................. 9

4.2 Social Acceptability.................................................................................................... 10

4.3 User Acceptability ..................................................................................................... 10

5 Results...................................................................................................................... 11

5.1 Technological feasibility ............................................................................................ 11

5.2 CO2 emissions reduction and capacity improvement ............................................... 12

5.3 Costs for reducing CO2 Emissions.............................................................................. 16

5.4 Social Acceptability.................................................................................................... 21

5.5 User Acceptability ..................................................................................................... 23

5.6 Cost effectiveness analysis........................................................................................ 24

5.7 CO2 Mitigation Costs ................................................................................................. 25

6 Conclusions .............................................................................................................. 25

List of Tables..................................................................................................................... 28

List of Figures.................................................................................................................... 28

References........................................................................................................................ 29

Annex A............................................................................................................................ 34

Annex B ............................................................................................................................ 40

Deliverable D6.1 iii

Abbreviations

Abbreviation Description

ABS Anti Blocking System

ACC Adaptive Cruise Control

AHS Automated Highway System

CAS Collision Avoidance System

CVIS Cooperative Vehicle-Infrastructure Systems

DAS Driver Assistance Systems

DGPS Differential Global Positioning System

ESC Electronic Stability Control

GDP Gross Domestic Product

GHG Greenhouse Gas

GJ Gigajoules

GPS Global Positioning System

ICT Information and Communication Technologies

ISA Intelligent Speed Adaptation

ITIF The Information Technology and Innovation Foundation

ITS Intelligent Transport Systems

Km kilometer(s)

LCA Lane Change Assistant

LDW Lane Departure Warning

MJ Megajoules

m/veh meters per vehicle

LB Lower bound

Lt litre(s)

NAHSC National Automated Highway System Consortium

NEDC New European Driving Cycle

OEM Original Equipment Manufacturer

passengers/h/l passengers per hour per lane

pkm/vkm Passenger per vehicle-kilometer

PATH Partners For Advanced Transit And Highways

SAVE System for effective Assessment of the driver state and Vehicle control in

Emergency situations (EU-funded project)

Deliverable D6.1 iv

TEN-T Trans-European Transport Network

UB Upper bound

VAT Value Added Tax

veh/h/l vehicles per hour per lane

V2I Vehicle to Infrastructure

V2V Vehicle to Vehicle

WP Work Package

Deliverable D6.1 1

Abstract

The TOSCA project aims to identify promising technology and fuel pathways to reduce

transportation-related greenhouse gas emissions through midcentury. An important building

block of this project is the techno-economic specification of low-GHG emission transportation

technologies, which are input into a scenario analysis. TOSCA considers all major modes of

passenger and freight transport, along with transportation fuels and technologies capable of

enhancing infrastructure capacity. This report is thus one out of a number of such techno-

economic studies.

In TOSCA Work Package 5 (WP5), technologies that could be implemented in the European

road and rail infrastructure over the next 40 years are evaluated both for passenger and

freight traffic. This deliverable (D6.1) examines technologies for road passenger transport that

could enhance mobility through improving the existing infrastructure capacity and, as a

secondary effect, could impact vehicle fuel consumption and GHG emissions.

In this report two Intelligent Transport Systems (ITS) technologies are evaluated for road

passenger transport: Driver Assistance Systems (DAS) and the Automated Highway System

(AHS). These technologies are anticipated to lead to a more efficient use of the existing road

network, while potentially reducing GHG emissions generated by individual vehicles. To assess

these technologies, we first define a reference system, which consists of the average new

passenger car in the road network of the EU-27 countries. Reference system characteristics

include vehicle fuel consumption, CO2 emissions and costs for the year 2009. Relevant

infrastructure capacity levels are also considered. After describing the various opportunities

offered by these technologies, the major constraints for reducing GHG emissions are

identified. Evaluation results are presented in terms of the basic dimensions of technological

feasibility, social acceptability and user acceptability.

Acknowledgement

We would like to thank Professor Amedeo Odoni for his numerous and valuable comments

that significantly improved the report.

Deliverable D6.1 2

1 Introduction

European Union countries experience 7,500 kilometers of traffic jams every day, with 10% of

the EU’s road network affected by congestion [1]. The fraction of time spent tied up in

congestion is even higher. As much as 24% of Europeans’ driving time is spent in traffic

congestion at a yearly cost of about 1% of the European Union’s GDP (approximately €115

billion) [2]. (Other studies, like P. Kopp and R. Prud’homme [3], suggest significantly lower

economic losses of about 0.03% of the GDP). These costs can be attributed mainly to loss of

time, but also to increased accident rates and more severe levels of air pollution.

It has been long recognized that any increase in road capacity, achieved by constructing new

highways provides only temporary relief from traffic congestion, while imposing a high

financial and environmental cost. One of the promising solutions suggested for this problem

are Intelligent Transport Systems (ITS). ITS technologies maximize the capacity of a given

physical infrastructure, thus reducing the need to build additional highway capacity. Overall,

ITS can reduce congestion by as much as 20% or more [4]. ITS also enable transportation

agencies to collect the real-time data needed to measure and improve the performance of the

transportation system, making ITS the centerpiece of efforts to reform surface transportation

systems and hold providers accountable for results [4]. ITS could also contribute to reducing

transport-induced CO2 emissions. Transport is a growing emitter of CO2. Between 1990 and

2007, transport sector CO2 emissions from within the EU-27 countries increased by 35.6%.

During the same period, road transport CO2 emissions have increased by 28.5%. Road

transport currently accounts for 70.9% of all transport related CO2 emissions in EU-27 [5].

The term ITS is used to describe systems which utilize a combination of computers,

communication, positioning and automation technologies to improve the safety, management

and efficiency of terrestrial transport, while reducing its environmental impact. ITS incorporate

four essential components:

• Vehicles, which can be located, identified, assessed and controlled using ITS;

• Road users, who employ ITS, for instance, for navigation, travel information and their

monitoring capabilities;

• Infrastructure, for which ITS can provide monitoring, detection, response, control,

road management and administration functions;

• Communications networks, to enable wireless transactions amongst vehicles and

transport users.

ITS applications increase safety, improve operational performance, particularly by reducing

congestion, enhance mobility and convenience, deliver environmental benefits, boost

productivity and expand economic and employment growth.

According to Ezell [4], ITS applications can be grouped within the following categories: 1)

Driver Assistance Systems (DAS) support drivers in maintaining a safe speed and distance,

driving within the lane to avoid overtaking in critical situations. 2) Advanced Traveler

Deliverable D6.1 3

Information Systems provide drivers with real-time information, such as transit routes and

schedules, navigation directions, and information about delays due to congestion, accidents,

weather conditions or road repair work. 3) Advanced Transport Management Systems include

traffic control devices, such as traffic signals, ramp meters, variable message signs and traffic

operations centers. 4) ITS-Enabled Transportation Pricing Systems include systems such as

electronic toll collection, congestion pricing, fee-based express lanes, and usage-based fee

systems (e.g. based on vehicle miles traveled). 5) Advanced Public Transportation Systems

that, for example, allow trains and buses to report their position so passengers can be

informed of their real-time status (arrival and departure information). 6) Cooperative Vehicle-

Infrastructure Systems (CVIS), are an enabler of automated highway systems (AHS), where

driving is computer-controlled using wireless communications between vehicles and

infrastructure. AHS provides the vision of “driverless” vehicles moving under automated

control.

Within the context of the above ITS taxonomies this report focuses on Driver Assistance

Systems (DAS) and the Automated Highway System (AHS). DAS have a considerable history. In

the late 1980’s, in Europe, several car manufacturers and research institutes carried out a

series of projects, such as PROMETHEUS (PROgraM for European Traffic with Highest Efficiency

and Unprecedented Safety) and DRIVE (Dedicated Road Infrastructure for Vehicle safety in

Europe), in order to determine the requirements and design standards for a class of ITS

applications, such as traveller information, vehicle control and safety systems [6, 7]. With

regard to AHS, the original research was performed by a team from Ohio State University.

Their first automated vehicle was built in 1962, and is believed to be the first land vehicle to

contain a computer. In the context of the PATH project, a prototype AHS was tested in San

Diego in 1997 [8].

This study suggests that DAS applications can contribute to a small (about 8%) increase in road

capacity and a small (around 5%) decrease of CO2 emissions from passenger cars. AHS, on the

other hand, could provide a significant improvement in road capacity, ranging from 2 to 2.5

times the current infrastructure capacity. At the same time, vehicle fuel consumption and CO2

emissions could decline by about 15-25%, due to mainly reduced aerodynamic drag and a

smoother traffic flow.

The characteristics, benefits, barriers to implementation and related costs are discussed in the

following sections.

2 Reference System Characteristics

The reference system is chosen to be the individually driven average new passenger car in

Europe (in 2009). Fuel consumption, CO2 emissions, costs and average annual distance

traveled for passenger cars are derived from the TOSCA WP1 report for road passenger

transport [9] and are shown in Table 1.

Deliverable D6.1 4

Table 1 Reference System Characteristics

REFERENCE PASSENGER CAR

Occupancy Rate pkm/vkm 1.5

Fuel type Petrol

Fuel Consumption lt/100km 6.2

CO2 Emissions gr/km 145

Retail Price €(2009) 16,500

Capital Costs €(2009)/year 660

Depreciation €(2009)/year 1,650

Operating costs (excl.fuel) €(2009)/year 1,260

Maintenance €/km

€(2009)/year

0.049

735

Parking and tolls €/km

€(2009)/year

0.013

195

Insurance €(2009)/year 330

Yearly Costs €(2009)/year 3,570

Average annual distance

traveled km 15,000

REFERENCE INFRASTRUCTURE CAPACITY

Road Capacity veh/h/l 1,800 [10]

Capacity passengers/h/l 2,700-3,600

REFERENCE ITS APPLICATIONS

Anti Blocking System (ABS)

Electronic Stability Control (ESC)

Table notes:

Vehicle retail price corresponds to the manufacture recommended price, in 2009, excluding VAT [9].

Maintenance costs include maintenance, replacement of parts, tires and service labour [9].

Insurance costs are assumed to be the 2% of the retail price of the vehicle [9].

The notions of occupancy rate, road capacity and reference ITS applications are discussed

below. The remaining concepts of Table 1 are presented in TOSCA WP1 report for road

passenger transport [9].

Occupancy rate depends on distance travelled. According to the 2001 U.S National Household

Travel Survey, for trips up to 50 km, it is slightly above 1.5 pkm/vkm, reaching 2 for distances

Deliverable D6.1 5

greater than 160 km and around 2.7 for distances greater than 1,000 km. In many European

countries, the occupancy rate of passenger cars ranges from 1.2 to 1.5 (pkm/vkm) [11, 12].

Road capacity is defined as the maximum sustained 15-min rate of flow, expressed in vehicles

per hour per lane (veh/h/l), that can be accommodated by a road under prevailing traffic and

roadway conditions in one direction of flow [10]. According to the Highway Capacity Manual

[10], highways can operate with capacities as high as 2,400 veh/h/l, under base traffic and

geometric conditions. In practice this cannot be achieved [12], resulting in lower capacities of

about 1800 veh/h/l. Road capacity issues are analyzed in detail in Annex A.

As indicated in Table 1, the reference passenger car is equipped with Anti Blocking System

(ABS) and Electronic Stability Control (ESC). These DAS applications are already standard in EU

countries. Some additional DAS applications have already been developed by Mercedes-Benz,

BMW, Volkswagen and Toyota but their deployment is still limited to premium cars. The

characteristics of the in-vehicle ITS applications and the current implementation status in

Europe are discussed in Sections 3 and 5 and in Annex A.

3 Technology Developments

This section describes ITS technologies that may be deployed in passenger cars and road

infrastructure by 2050. Emphasis is given to DAS and AHS technologies.

3.1 Driver Assistance Systems (DAS)

Driver Assistance Systems is a collective name for a whole range of Information and

Communication Technology (ICT) in-vehicle systems which support drivers in maintaining a

safe speed and distance, driving within a lane to avoid overtaking in critical situations. In

summary, they inform and warn the driver, provide feedback on driver actions, increase

comfort and reduce the workload by actively stabilising or manoeuvring the car. In TOSCA

WP5, we examine the following core systems:

• Anti Blocking System (ABS), which prevents the wheels of the vehicle from locking up

while braking;

• Electronic Stability Control (ESC), stabilizing the vehicle and preventing skidding;

• eCall, which automatically calls emergency services and transmits location data from

the scene of an accident;

• Adaptive Cruise Control (ACC), maintaining a preset distance to the vehicle ahead and

adjusting driving speed automatically;

• Lane Departure Warning (LDW) systems, warning the driver when the vehicle begins

to move out of its lane (unless a turn signal is on in that direction) on highways;

• Lane Change Assistant (LCA) systems, which continuously monitor the rear blind spots

on both sides of the vehicle;

Deliverable D6.1 6

• Intelligent Speed Adaptation (ISA), also known as Intelligent Speed Assistance, which

is a system that constantly monitors vehicle speed and the local speed limit on a road

and implements an action when the vehicle is detected to be exceeding the speed

limit;

• Collision Avoidance System (CAS), operating with a sensor installed at the front end of

a vehicle. The sensor scans the road ahead for vehicles or obstacles. When an obstacle

is detected, the system decides whether collision avoidance action is needed and a

manoeuvre is undertaken.

Most DAS applications that have been implemented in the market are based on purely in-

vehicle technology (e.g. ABS, ESC). However, other DAS (such as ISA) require some

components outside the vehicle, for instance for precise vehicle positioning. It is expected that

future DAS applications will be based on a combination of in-vehicle and infrastructure based

technology. This will significantly increase the complexity of the implementation of these

systems.

All these technologies are expected to improve highway operation by providing drivers with a

more relaxing driving environment resulting in lower rates of accidents and congestion.

Response time of emergency services is cut drastically by eCall reducing fatalities by 5-10%

[14]. As a side-effect, eCall and other applications such as ESC and Intelligent Speed

Adaptation (ISA) can reduce traffic congestion because 15% of all congestion in Europe is due

to accidents [15]. Moreover, if eCall were to be deployed on all vehicles, it could trigger a

decrease in fatalities by 5-15% across EU-27 by 2020 and a reduction in severe injuries by 10-

15% [16].

Vehicle manufacturers, such as BMW and Mercedes, have already installed DAS applications in

their cars. Examples include Electronic Stability Control (ESC), Adaptive Cruise Control (ACC),

and Anti Blocking System (ABS). Vehicle-to-Vehicle (V2V) communications systems are also

under development [17, 18, 19]. Data for the deployment status of DAS applications is given in

Annex A. Table 2 summarizes the historical market introduction of key DAS technologies.

Table 2 Introduction of DAS applications in the EU-market

DAS

application

Market Introduction in

EU (a)

Requirements

(b) Diffusion

(c)

Anti Blocking

System (ABS)

1978 (standard

equipment in Europe)

Wheel speed sensors,

Brake system interface

All new European vehicles

Electronic

Stability

Control (ESC)

1999

(wide penetration in

Europe)

Sensors,

Interface to the braking

system

All OEMs, but not in all lines

By 2012-2014 standard equipment

(according to EC2008 regulation)

Deliverable D6.1 7

eCall 2003

(for premium cars)

Link to emergency

services for position of

crashed vehicle and

identity

Partially on market: BMW, Lexus.

Optional but may become

mandatory

Adaptive Cruise

Control (ACC)

2005 Direct vehicle to vehicle

communication

First installed in Mercedes-Benz

Optional as comfort function

On market: BMW, Audi, Lexus

Lane Departure

Warning (LDW)

2005 Lane recognition Optional equipment sold as safety

function. On market: on a number

of vehicle models from Citroen to

Mercedes-Benz.

Lane Change

Assistant (LCA)

2005 Lane recognition ,

Blind spot monitoring,

Rear sensing

Partially on market: Audi, Volvo-

warning and only small steering

force

Optional as comfort function

Intelligent

Speed

Adaptation

(ISA)

2006 (as warning

function-based on

digital maps)

After 2015 (as an

adaptive function)

Digital maps,

Positioning system,

Environmental data,

Information on

vehicle limits

Several systems for speed limit

advice are available

May become mandatory

BMW: already installed it

Mercedes: in few years

Collision

Avoidance

System (CAS)

After 2015 Predictive sensing Optional as safety function with

potential to become mandatory.

Table notes: (a) Sources: [20, 21, 22, 23, 24]

(b) Source: [23, 24]

(c) Sources: [17, 18, 19, 21, 23, 25]

3.2 Automated Highway System (AHS)

Cooperative Vehicle-Infrastructure Systems (CVIS) are an enabler of the Automated Highway

System (AHS), where driving is computer-controlled using wireless communications between

vehicles and infrastructure. In automated highways, vehicles can organize themselves into

platoons and be linked together in communication networks, which allow the continuous

exchange of information about speed, acceleration, braking and obstacles. The first vehicle of

a platoon is called the platoon leader and its role is to manage the platoon and guide it on the

road. The other vehicles are called followers and their main goal is to maintain a specific

distance in time from the preceding vehicle using information from sensors. An AHS provides

the vision of “driverless” vehicles moved under automated control. It has the potential to

increase the capacity of existing highways (vehicles per hour per lane moving along the

Deliverable D6.1 8

highway) and thus reduce highway congestion. Since platooning enables vehicles operating

much closer together than is possible under manual driving conditions, each lane can carry

roughly twice as much traffic as it can today. Figure 1 shows the eight-car platoon,

demonstrated by the National Automated Highway System Consortium (NAHSC) in San Diego

in 1997.

Figure 1 Configuration of an eight-car platoon [26]

An AHS includes three different control systems. A longitudinal control system maintains

speed and spacing accuracy between the vehicles through the use of radar and radio

communication between cars. Each car in the platoon uses its radar to measure the distance

to the preceding car. The longitudinal control system enables short spacing between the cars

and thus increases highway capacity. What is more, the maintenance of a constant speed of

the platoon leads to a smooth ride. In addition, a lateral control system (or steering control

system) uses several technologies and aims to keep the vehicle within the dedicated lane. It

relies on sensors placed on the infrastructure and the vehicles. Each vehicle of the platoon

uses its sensors in order to determine its location with respect to the road markings and

geometry. Finally, a fault management system detects and handles failures in the sensors on

the vehicles. As a result, in case of a failure detected in any vehicle, the fault management

system is activated and controls the other cars of the platoon to avoid a crash.

Technology Trajectories

Figure 2 shows the envisioned technology trajectories for road passenger transport. DAS are

already present in the form of driver information systems providing guidance, warnings and

alerts to drivers. The next generation of DAS will enable automatic vehicle response to

warnings. Collision Avoidance Systems (CAS) fall in this category. In contrast, the AHS is

expected to emerge from the development of cooperative vehicle infrastructure (CVIS)

platforms that enable the exchange of information between vehicles as well as vehicles and

infrastructure. DAS systems will form an important component of AHS and will continue to

evolve. The vision of “driverless” vehicles moved under autonomous control is not expected to

materialize before 2030.

Deliverable D6.1 9

Figure 2 Technology trajectories for road passenger transport

4 Constraints on reducing GHG emissions

In this section, constraints on implementing the ITS technologies discussed above are

evaluated from three different aspects: technological feasibility, social acceptability and user

acceptability.

4.1 Technological feasibility

Within this area, challenges that need to be addressed include system interdependency,

network effect, application scale, funding, and political and institutional challenges. Many ITS

applications are interdependent, require system coordination to deploy, and must operate at a

sufficiently large scale to be effective. Regarding DAS, no significant technology constraints are

foreseen. AHS on the other hand must be integrated and at the same time adopted by

individual users to be effective.

There are two main requirements for the development and uptake of AHS, namely

interoperability and standardization. Interoperability issues should be addressed to enable

multiple services provided over multiple different platforms, which will work in different

countries (as vehicles can easily cross borders), while maintaining a simple-to-use interface

that requires minimum intervention from the driver. Standardization will eventually achieve

integration of vehicle and transport infrastructure. This requires open in-vehicle platform

architecture and safe human-machine interfaces, R&D advances in cooperative systems and

specifications for Infrastructure-to-Infrastructure, Vehicle-to-Infrastructure (V2I) and Vehicle-

to-Vehicle (V2V) communication. Due to the cross-border operation, interoperability and

standardisation issues have to be tackled at the EU level. Without Europe-wide coordination

and quality control, the functioning of any application in this field would depend on local or

national initiatives, and there would be a risk that customers, increasingly relying on such

support, will be confused when arriving in areas where the information is less reliable or even

absent.

Deliverable D6.1 10

4.2 Social Acceptability

Social equity implications: Since large amounts of public funds could be spent to deploy an

AHS, social equity issues must be addressed. A key question is whether it would be fair and

politically feasible to dedicate travel lanes to automated vehicles if many low-income drivers

cannot afford automated vehicles. Thus, concerns have been raised about creating two classes

of users-those who can afford to pay for AHS and those who cannot-with deleterious effects

for drivers who cannot pay.

Passenger Safety: The development of DAS and AHS significantly increases passenger safety.

However, negative safety consequences from ITS may also be encountered, as these systems

require additional equipment on the vehicle, such as in-vehicle screens, which may distract the

driver’s attention from his driving task [27]. When the “driverless” AHS vision was discussed in

the European Parliament, concerns and scepticism were raised regarding safety. During the

initial phase of ITS implementation, an adaptive period would be required so that drivers get

used to the new technologies. It should be completely clear for the driver when and how to

use various systems. Research on driver behaviour towards the use of these systems is under

way for the identification of the overall safety impact.

Privacy: Data privacy is a key factor for the future success of ITS technologies. Some of the

data will be personal data (for example, records of various payments) that may reveal user

identity. Other will be non-sensitive data such as velocity and length of trip. To avoid

unauthorised access and abuse of personal data, public authorities on national and

international levels should introduce privacy protection laws.

Liability issues: Liability issues have unique implications in the context of ITS technologies. As

the control of the vehicle is transferred from the driver to the “ITS elements”, an allocation of

responsibility might be made among the driver, vehicle manufacturer and the roadway

authority when an accident occurs. The legal community and the insurance industry will

undoubtedly be consulted extensively in considering the importance of legal and liability

issues associated with DAS and AHS technologies. It is clear that all parties involved, such as

drivers, manufacturers and roadway authorities, have a basic responsibility for safe traffic and

the use of ITS therein. A key question is how willing will car manufacturers be to accept the

potential liabilities involved in selling cars that drive themselves.

The resolution of data security and protection, and of privacy and liability issues may hinder

the uptake of certain advanced ITS services. These issues have been identified as being core to

the current slow uptake of ITS: in the absence of clear rules and responsibilities, neither

providers nor customers will be willing to invest or buy.

4.3 User Acceptability

A basic question in ITS implementation is user acceptability. Handing over control to a device is

evaluated as a negative aspect of DAS and AHS. According to a questionnaire survey

Deliverable D6.1 11

conducted in the SAVE project1 [28], drivers expressed concern about “loss of control” and

“driverless cars” which results in inconvenient driving. Drivers stated that they are reluctant to

release vehicle control and are willing to hand over control only in cases of emergency, such as

driver breakdown [28]. According to De Vos and Hoekstra [29], short spacing between vehicles

in platoons is also considered less comfortable and less accepted than larger headways

between vehicles [29]. However, the discomfort experienced due to short headways may

decrease after some time, as people get used to AHS driving. According to some research

findings from PATH program [7], once vehicle passengers have experienced 1 or 2 minutes of

AHS driving within a platoon, they become very comfortable with the experience.

5 Results

5.1 Technological feasibility

ITS are enabling technologies whose deployment is subject to a variety of conditions, including

maturity of technology, market acceptance or willingness to invest and buy, and is dependent

on actions being implemented in a coordinated way by various independent stakeholders.

Several DAS applications are already available on passenger cars and therefore the lower

bound in Table 3 is 2010 (see also Table 2). Following most examples of new vehicle systems,

initial fitment levels will be small and are likely to be in premium vehicles. The European

Commission has adopted an action plan suggesting targeted measures and an implementation

framework that will speed up their market penetration in Europe [20]. However, the full

potential of DAS will be realized only with large-scale deployment in vehicles. This requires

further regulatory actions. For example, the European Commission has proposed that all new

cars from 2012 on should have ESC systems, to drastically improve vehicle safety [21].

While the deployment of further DAS technologies is currently being pursued, the integration

of AHS requires major developments in two main directions: automation in vehicles and

automation in the road infrastructure. Although the first component already exists to some

extent the emphasis is on adapting and combining the separate ITS technologies. Initial

deployment and operation is expected to focus on high priority routes located in high demand,

major urban and inter-city corridors [30]. Table 3 summarizes the technological feasibility of

DAS and AHS for passenger cars.

1 The questionnaire of the SAVE Project was distributed to 407 drivers from 9 European countries.

Deliverable D6.1 12

Table 3 Technological Feasibility

Market-readiness R&D Requirements

Most Likely LB UB Insignificant

Significant

(company-level)

Substantial (EU-

wide program)

DAS 2015 2010 2020 X

AHS 2030 2025 2050 X

5.2 CO2 emissions reduction and capacity improvement

DAS technologies improve road capacity and reduce delays through speed and headway

adjustment. There are two ways in which congestion can be reduced when implementing DAS.

First, congestion reduction is a side effect of safety improvements. Usually, road accidents

impede the flow of traffic until rescue services have provided first aid to the accident victims

and the police have documented the incident. DAS limit accident severity and improve the

efficiency of the rescue chain (e.g. eCall). Thus the accident site is cleared more quickly and

congestion is relieved. The second effect on congestion is due to improvements in the traffic

flow. Some DAS, especially those which have an impact on the longitudinal control of the

vehicle, reduce variations in acceleration. As a consequence, the flow of traffic is smoother.

When a substantial part of the total vehicle fleet is equipped with DAS, noticeable capacity

improvements may result.

For DAS, the direct effects on road capacity are expected to be small when penetration rates

are small [25]. The main reason for this is that the travel time changes at the network level are

mostly small (in the order of seconds) and hardly noticeable to drivers. Capacity improvements

of about 7-8% in comparison with manual driving conditions due to the decrease of headway

are reported by Zwaneveld et al [31] (depending greatly on the penetration rate and the

headway setting). If we consider that manual driving results in capacities of 1800-2400

veh/h/l, driving with DAS would achieve maximum capacities of 1900-2600 veh/h/l.

Nevertheless, a study by Minderhoud [32] indicated that DAS applications, such as ACC, offer

an increase of road capacity only under strict conditions on market penetration and use.

Furthermore, DAS can contribute to fuel consumption and CO2 emission reductions. Some DAS

applications (such as ACC and ISA) have a direct effect on CO2 emission reduction through

changes in speed, while other (such as eCall, LDW and LCA) reduce CO2 emissions indirectly by

alleviating the congestion caused by accidents (as explained above). For example, ISA is

expected to contribute to a reduction of fuel burn and CO2 emissions by 2-5%, as it can reduce

the severity of accelerations and decelerations and stop-and-go transients [24, 33, 34, 35]. The

expectations of the effects of ISA on the environment are based on the reduction and the

homogenization of driving speeds. For the ACC system, effects of 0.5-10% on fuel burn and

CO2 emission reduction are reported in the literature since the system maintains a constant

Deliverable D6.1 13

speed and unnecessary “speed-ups” and abrupt braking are avoided2 [24, 36, 37, 38]. On the

other hand, the implementation of eCall, LDW and LCA has an indirect effect on CO2 emission

reduction by avoiding accidents and so avoiding subsequent queue formation (reducing the

amount of congestion and the emissions produced) [24, 36]. These indirect effects depend on

the number of avoided accidents and the amount of avoided congestion. However, this is very

hard to estimate. Finally, there are other DAS applications, such as ABS, that are not reported

to have effects on fuel consumption and CO2 emissions. This is the reason why the Lower

Bound on fuel consumption and CO2 emission reduction by DAS shown in Table 4 is zero.

The deployment of AHS is expected to increase road capacity in two ways. The most significant

is by reducing distances between fully automated vehicles until they reach the minimal safe

distance (considering the controllers’ capabilities and the road condition). This results in more

vehicles in a given lane. In addition, the AHS concept stabilizes the traffic flow and provides

the vehicles with conditions of constant cruise speed. Thus, traffic equilibrium can be reached

avoiding stop-and-go operations and inefficiencies caused by inattentiveness, merging,

weaving, and lane changing [38]. The capacity benefits offered by AHS depend on the platoon

configuration. Key parameters include the platoon size and the inter-vehicle and inter-platoon

separations. Various platoon sizes have been reported in the literature ranging from 5 to 20

vehicles per platoon. To maximize throughput it is desirable to form platoons that are

reasonably long [39]. The vehicle mix is another factor that influences the potential capacity of

a platoon (see Figure 3). Introducing even small percentages of trucks or buses to the flow of

passenger cars can significantly reduce the achievable capacity because of the poorer

performance of heavier vehicles [40]. For example, when the mix consists of 93% passenger

vehicles, 6% trucks and 1% buses, maximum capacity can be up to 4,900 veh/h/l. Finally, the

capacity of the AHS is affected by the length of the trip operated in the platoons and the

frequency with which vehicles enter and exit platoons. Short trip lengths and frequent entries

and exits limit capacity improvements. Conversely larger journeys (for example, up to an hour)

and a large percentage of total journey spent in platoons offer higher benefits [13]. A study by

Randolph et al [39] argues that platoon configuration should ensure that platoons remain

intact for considerable distances and proposes sorting the platoons depending on the

destination.

2 The real effect of ACC on emissions strongly depends on the behaviour of the driver. A fuel-efficient

driver may not be surpassed by the DAS.

Deliverable D6.1 14

Autonomous: vehicles are equipped with ITS-technologies, but not vehicle-to-vehicle communication, Low

Cooperative: vehicles communicate with each other only in cases of emergency, High cooperative: vehicles

continuously communicate with each other.

Figure 3 Simulated road capacity for AHS operation [41]

Figure 4 shows the potential benefits of AHS as derived in [12]. It assumes a vehicle length of

5m and intra-platoon spacings of 0.1 seconds. Capacities of 8,000 veh/h/l are obtained when

the platoon size is 16 vehicles. The practical considerations discussed in Section 2 suggest

taking the 80% of the theoretical value as a realistic figure. Therefore, capacities of 6400

veh/h/l could be achieved for platoons of 16 vehicles. The highest capacity values are achieved

at the highest speed provided the platoon size is large. Higher speeds require larger headway

and thus lead to small capacity increase.

Deliverable D6.1 15

Figure 4 Lane capacity in relationship with speed and platoon size

Another study is the platoon demonstration designed by researchers at the California PATH

program in 1997 [8]. The vehicles were organized in eight-vehicle platoons and travelled at a

fixed separation distance of 6.5 meters and at a speed of 105 km/h (0.2 seconds). The AHS

capacity which represents a “pipeline” capacity was about 5700 veh/h/l. Consideration of

divergence from ideal conditions and disturbances from vehicles entering and exiting the

highway results in a 20%-25% reduction and leads to an effective throughput of about 4300

veh/h/l.

In summary, estimation of the expected throughput for an AHS is a challenging task and has

been the subject of several studies [8, 13, 24, 41, 42, 43]. These studies show very high

capacity improvements with capacities up to 8000 veh/h/l as opposed to manual driving

conditions where capacity levels would be approximately 1,800 veh/h/l (see Annex A).

The high improvements on capacity refer to the vehicles that operate in platoons. However,

platoon operation will reduce the congestion of the adjacent lanes as well, as a percentage of

the whole traffic will be moved from the manual traffic to the platoons. The entry and exit of

vehicles to and from a high-capacity AHS lane can be accomplished without adverse impacts

on adjacent freeway lanes by use of dedicated entry and exit ramps, while the transitions

between manual and automatic vehicle control can be managed through electronic “check-in”

and “check-out” processing at these ramps [7, 40]. Nevertheless, the overall congestion

reduction depends on the penetration level of the AHS operation.

On the other hand, whilst the use of dedicated traffic lanes may greatly improve the

performance of the car fleets which use them, the resulting impacts on other vehicles, due to

reduced road space, could well counteract this advantage.

Besides capacity improvements, AHS offers a noticeable reduction in travel time. The study [4]

reports that in vehicle information and communication technologies can reduce travel time for

long trips by an average of 20%. Analysis of the Long Island Expressway and the Capital

Deliverable D6.1 16

Beltway network near Washington DC led to travel time reductions by 38% to 48% [44].

Another study [45] finds that the implementation of AHS can cut travel time by 33% to 50%.

AHS offers increased safety and lower fuel consumption when compared with individually

manoeuvred cars. The reason is that the cars in the platoons are close to each other, and

exploit the resultant lower air drag. The longitudinal control system can also reduce the

severity of accelerations and decelerations and stop-and-go transients thereby reducing fuel

use and emissions. The estimated fuel burn and CO2 emissions reduction for platoon

operations is 15-25%, depending on the number of vehicles in the platoon and the spacing

between them [8, 46]. This reduction should only apply to roads over which AHS will be

implemented. When comparing AHS driving with manual driving operating at free-flow, the

average fuel consumption reduction is low. However, when comparing it with manual driving

under congested conditions, the average fuel consumption reduction is high, because of the

smoother traffic flow associated with AHS [47]. Therefore, it should be clear that the high

benefits of AHS on fuel burn and emissions reduction are achieved at congested areas. The

above remarks are summarized in Table 4. Fuel consumption and CO2 emissions reduction

estimates are based on the literature review. However, some of the findings were also verified

by the views of experts that were interviewed in the context of the TOSCA project (see Annex

B).

There is a direct and proportional relationship between fuel consumption and CO2 emissions.

An improvement in terms of fuel efficiency would be immediately translated into a reduction

of CO2 emissions and vice versa (when related to road traffic and fossil fuel) [48]. This is the

reason why Table 4 indicates the same estimates for fuel consumption reduction and CO2

emission reduction for each technology.

Table 4 Technology Characteristics

Capacity (veh/h/l) Fuel Consumption Reduction CO2 Emissions Reduction

Most

Likely LB UB

Most

Likely LB UB

Most

Likely LB UB

DAS 2000 1900 2600 5% 0% 10% 5% 0% 10%

AHS 4300 4300 6400 20% 15% 25% 20% 15% 25%

5.3 Costs for reducing CO2 Emissions

In this section the retail price and the yearly costs of the proposed technologies are assessed.

The retail price of the ITS-equipped car consists of the price of the reference car (as given in

Table 1) and the extra unit costs needed to implement the ITS elements. The yearly costs of

the ITS-equipped cars include capital, depreciation and operating costs.

Various studies [4] have estimated that the benefit-cost ratio of ITS may range from 6:1 to 9:1,

far above the addition of conventional highway capacity, which has a benefit-cost ratio of

Deliverable D6.1 17

2.4:1 [49]. For example, a study of a model ITS deployment in Tucson Arizona, consisting of 35

technologies was conducted in 2005 [4]. These technologies would cost $72 million to

implement while the average annual benefits to mobility, environment, safety and other areas

were estimated to be $455 million annually, meaning a 6.3:1 benefit-cost ratio [4].

When assessing the costs associated with the implementation of ITS, individual drivers are

considered to be the end users and thus would need to carry the costs. In this report the costs

refer to those associated with the car which is equipped with ITS elements.

A cost estimate of each separate DAS system is speculative because rarely these elements are

installed in the car as a stand-alone option. Instead, they are frequently bundled with other

technologies. The costs do not reflect the possibility of shared use by other ITS applications.

Instead, the conservative position has been taken that all electronic costs are attributed to

DAS devices. Table 5 provides indicative values of unit costs for DAS applications as specified in

the literature [23, 50, 51, 52]. These costs reflect the cost to the original equipment

manufacturers (OEM) plus a mark-up for covering implementation costs on the vehicles. The

retail price (what the end user faces when purchasing the application) would be higher

because it would also include profit margins. As a rule of thumb, it can be stated that costs

have to be multiplied by a factor of 3 to reflect the retail price of DAS applications [50, 52].

Table 5 Unit costs and retail prices for DAS applications

DAS systems Unit costs (€/vehicle) Retail Prices (€/vehicle)

Electronic Stability Control (ESC) 150 400

eCall 60 180

Adaptive Cruise Control (ACC) 80-160 250-450

Lane Departure Warning (LDW) 300 800

Lane Change Assistant (LCA) 150-300 450-900

Intelligent Speed Adaptation (ISA) 230 600

Table notes: The retail prices have been derived from the stakeholder analyses done by the eImpact

Project [52].

The equipment of AHS-equipped cars includes vehicle longitudinal and lateral control,

advanced steering and cruise control and systems needed for platoon operations. To assess

the retail price and the yearly costs for the AHS-equipped car, we used cost data from the

Research and Innovative Technology Administration (RITA) [51].

We note that the above cost estimate could be speculative as it depends on the assumptions

made about what components will have already been installed on the vehicles for other

purposes (such as DAS systems) and can be re-used for AHS as well, versus which components

are needed only for AHS. For example, GIS software is already used for navigation and some

DAS systems, and will be used even more in the future. The AHS may need a higher level of

detail in the database, so there could be some modest incremental cost. Sensors for lateral

Deliverable D6.1 18

and longitudinal control may have already been installed on vehicles for Lane Departure

Warning (LDW) and Adaptive Cruise Control (ACC) respectively, but some additional

capabilities may be needed for AHS in platooning, so there could be a modest incremental

cost. Consequently, we assume that a variety of ITS elements will probably be present on the

vehicles and most of the additional investment will be in software.

Table 6 presents the costs for AHS equipment, considering all the assumptions mentioned

above. Unit costs represent the costs for obtaining ITS components plus the costs for

integrating them on the vehicle.

Table 6 Unit costs and maintenance costs for AHS elements in passenger cars

Subsystem Element Unit costs (€/vehicle) Maintenance Costs

(€/vehicle/year)

Mean LB UB Mean

Communication Equipment 190 130 260 5

GIS Software 120 100 140 0

Sensors for Lateral Control 350 290 410 10

Sensors for Longitudinal Control 190 140 240 5

Advanced Steering Control 200 180 220 5

TOTAL 1,050 840 1,270 25

Therefore the retail price of the AHS-equipped car is the reference retail price (equal to

16,500€, given in Table 1) plus the mean value of unit costs shown in Table 6 (equal to 1,050€).

Yearly costs include capital cost, depreciation and operating costs. Capital costs can be

expressed by the equation:

Capital costs = r · I

where I denotes initial investment [€] and r discount rate (equal to 4%). Depreciation costs can

be expressed as:

Depreciation costs = I/n

where n is the life of the vehicle [in years] (equal to 10 years). The initial investment

represents the retail price of the AHS-equipped car. Thus, the yearly costs can be calculated as:

Yearly cost = r · I + I/n + O

where O denotes operating costs [€/year]. Operating costs include maintenance, parking,

tolls and insurance costs.

Maintenance costs of the AHS-equipped car (see Table 6) are estimated to increase by 25

€/year (mean value) due to maintenance of the extra ITS elements (in comparison with the

reference car). Toll costs are expected to increase with AHS services to account for the

additional AHS infrastructure investments and maintenance. An average increase by 10% of

Deliverable D6.1 19

tolls cost of the reference car (which is presented in Table 1) is assumed. Thus, parking and

tolls costs of the AHS-equipped car3 are estimated to be about 0.0143 €/km. Considering the

above, maintenance costs will be approximately 760 €/year, parking and tolls costs will be

around 220 €/year.

Insurance costs of AHS compatible vehicles are uncertain. On one hand, the accident rates are

expected to decline as a result of AHS operations, and thus lead to reduced annual insurance

costs for the AHS-equipped cars. On the other hand, liability issues remain legally uncertain

(see Section 4.2) which may result in an increase of insurance costs. Insurance costs are

assumed to be 2% of the retail price (as assumed for the reference car), resulting in 350

€/year. These computations are presented in Table 7. Lower and upper bounds are indicated

in Table 6. It is noted that operating costs do not include fuel costs.

Table 7 Operating costs after AHS implementation

Maintenance Parking & Tolls Insurance

Operating costs

€(2009)/year

Reference car 735 195 330 1,260

AHS

Most likely 760 220 350 1,330

LB 760 220 345 1,325

UB 760 220 355 1,335

The results of the retail price and the yearly costs of an AHS-equipped car are presented in

Table 8.

Table 8 Retail price and yearly costs of the AHS-equipped car

Retail Price

€(2009)

Capital costs

€(2009)/year

Depreciation

€(2009)/year

Operating

Costs

€(2009)/year

Yearly Costs

€(2009)/year

Reference car 16,500 660 1,650 1,260 3,570

AHS

Most likely 17,550 700 1,755 1,330 3,785

LB 17,340 695 1,735 1,325 3,755

UB 17,770 710 1,780 1,335 3,825

3 Parking and tolls costs are assumed to increase only because the toll costs increase. Parking costs are

assumed to remain constant after the AHS implementation.

Deliverable D6.1 20

There are large costs in the design, development, testing and production of the components

and subsystems that comprise AHS. This means that the prices that have to be paid by vehicle

buyers (end users) cannot come down to moderate levels until there is a substantial

production volume (unless the suppliers decide to subsidize the early adopters because they

are confident of a large return on their investment later). The dominant consideration in

achieving unit cost reductions therefore has to be increasing the number of vehicles to be

equipped each year. But, suppliers usually follow a principle, beginning with small volumes but

higher value users and then advancing step by step to higher volume users who will be able to

pay successively lower prices. The key challenge is ensuring that at each step along the way,

these vehicle users achieve sufficient benefits from the system to justify the price that they

have to pay.

Figure 5 presents the trends in the retail price of the ITS elements over cumulative production.

The assumption is that the learning rate of ITS elements follows that of other electronic

elements such as computers. The learning rate used is 1% after doubling the cumulative

production [53]. Hence, after producing ITS equipment for the first 200,000 vehicles, the

production costs would decline by about 20% from its original value.

Trends of Retail Price of ITS elements

50

55

60

65

70

75

80

85

90

95

100

0 200.000 400.000 600.000 800.000 1.000.000 1.200.000

Production Quantity

%

Figure 5 Trends in retail price of ITS elements over cumulative output

Table 9 gives an overview of the retail price of some DAS elements in the years 2010 and 2020.

A reduction of retail prices from 2010 to 2020 is expected. Prices of both ESC and LDW are

predicted to decline by 25%. The retail price reduction for eCall and ISA is 16.7% [52].

Deliverable D6.1 21

Table 9 DAS retail prices in 2010 and 2020

Retail Prices

System 2010 2020 Percentage change

ESC 400 300 -25%

LDW 800 600 -25%

eCall 180 150 -16.7%

ISA 600 500 -16.7%

We assume that the yearly costs of an AHS-equipped passenger car will increase over time at

the same rate as for the reference car. Capital, depreciation and insurance costs depend on

the retail price, so it is important to estimate the evolution of the retail price of the passenger

car. The retail price is assumed to remain constant over time, while maintenance costs and

parking and tolls costs are assumed to decrease by an annual rate of 1%. The trends of the

retail price and maintenance costs are based on EU historical data and are in line with TOSCA

WP1 report on passenger cars [9]. The results are presented in Figure 6.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

2010 2015 2020 2025 2030 2035 2040 2045 2050

Yearl

y Cost

s (€

2009/y

ear)

m

Figure 6 Trends in yearly costs of an AHS-equipped vehicle

5.4 Social Acceptability

When implementing ITS technologies, social acceptability aspects have to be taken into

account. The implementation concept has to be accompanied by actions that reduce barriers

to adoption and acceptance. In the following paragraphs we analyse social aspects that must

be taken into consideration when deploying ITS in Europe. These include economic growth and

generation of jobs within EC, passenger safety and comfort and privacy issues. Other aspects

such as social equity implications and liability issues have already been discussed in Section 4.

Deliverable D6.1 22

Economic growth and generation of jobs within EC: ITS will be an important growth industry

over the next 25 years. Experts predict that the cumulative global market for ITS-related

products and services will reach €300 billion in 2017 [54]. A number of countries, including

South Korea, Germany, and Japan, view ITS as a key industrial sector, capable of generating

considerable export-led economic and employment growth [5, 55].

Investments in ITS applications will create technology sector jobs for engineers, electronics

technicians, software developers, and system integrators. According to some estimates, about

50% of ITS project spending will be for direct labour as compared to 20% for new highway

construction [56]. The U.S. Department of Transportation estimates that the field of ITS could

create almost 600,000 new jobs over the next 20 years. A 2010 ITIF study [4] found that a €6.5

billion investment in ITS in the United Kingdom would support approximately 188,500 new or

retained jobs for one year.

Passenger Safety and comfort: The development of DAS and AHS is reported to increase

passenger safety. Vehicle-based sensors can detect hazardous conditions and computers can

take actions faster than drivers can. V2V communication enables vehicles that encounter

problems to “warn” other vehicles to take action. The stability that ITS technologies offer to

the vehicle also lowers the acceleration and deceleration regimes of each vehicle, and thus

reduces the probability of collisions, often caused by unstable traffic flow leading drivers to

apply high decelerations [38]. On the other hand, there are some safety issues that could be

investigated when implementing ITS. Car drivers using ITS may have false expectations from

the assisting function of ITS elements. Some DAS technologies might generate unexpected

driver behaviour (less alertness and over-expectations) which could eliminate positive safety

impacts [57, 58].

Privacy: Privacy issues have already been mentioned in Section 4.2. A survey conducted within

the CVIS Project examined the end-user's perception of present and future DAS technologies

on road transport. Data privacy was one of the aspects examined. The results indicated that

when drivers were asked about data privacy issues, around 60% stated that they would not

use these systems in case they would invade their privacy. Only 23% of respondents did not

mind the fact that the systems could invade their privacy since they consider the systems very

useful. However, when only car data are involved (no personal data), 60% of respondents

would agree to be geographically tracked [59]. In general, 60% of European drivers4 declared

to be willing to collaborate with some restrictions, for instance, as long as no personal data are

involved [59].

Table 10 presents the results of social acceptability for DAS and AHS. The ratings are from “--”

which corresponds to “significantly worse than reference” to “++” which corresponds to

“significantly better than reference”, with “0” meaning “comparable to reference”.

4 The survey was distributed among 12 European countries (Norway, Croatia, UK, Switzerland, Germany,

The Netherlands, France, Portugal, Belgium, Spain, Austria and Italy).

Deliverable D6.1 23

Table 10 Social Acceptability of DAS and AHS

So

cia

l e

qu

ity

imp

lica

tio

ns

Ge

ne

rati

on

of

job

s

wit

hin

EC

Pa

sse

ng

er

Sa

fety

an

d c

om

fort

Pri

va

cy

Lia

bil

ity

DAS - to 0 + + 0 -

AHS -- ++ + -- -

5.5 User Acceptability

ITS related surveys [59] show that ITS applications are overall positively evaluated. However,

percentages decrease when drivers surveyed had to pay for these systems. The percentage of

acceptance decreases by at least 25% on average when the end user is asked about willingness

to pay for the systems [59]. Consequently price could be a restrictive factor for widespread

adoption of ITS among road users. Today most ITS systems can be found mainly in premium

cars (see Annex A for the penetration rates in Europe). Middle and lower income classes would

not benefit from any advances if applications are too expensive. Platoon operations save

travel time for traffic overall and particularly for those vehicles in the dedicated lane. The

benefit of AHS with respect to time savings is the time difference between the results with and

without AHS driving conditions. A study by Juan et al [60, 61] examined a case of a three-lane

motorway with a dedicated lane for AHS-equipped cars. Among the various benefits of AHS

driving, they examined the time savings due to AHS in comparison with manual driving, using a

microscopic simulation model. The results showed that time savings occur when at least 30%

of total highway traffic is served by AHS5.

Acceptability of the DAS and AHS to the end users is rated and presented in Table 11 (ratings

are from “0” corresponding to “no adoption” to “5” corresponding to “full adoption”).

5 This fact could be a barrier for acceptance of the technology at the beginning of the deployment of

AHS (where penetration level may be lower than 30%) making it difficult to get enough users to adopt

the technology to exceed the 30% level. Thus, there should be a subsidy-based policy to get enough

users to adopt the technology so that AHS does provide significant time savings.

Deliverable D6.1 24

Table 11 User Acceptability at given User Cost

User Acceptability at given User Cost

DAS 4

AHS 4

5.6 Cost effectiveness analysis

The cost-effectiveness of AHS technology compared to the reference car is studied as break-

even costs. The break even oil costs are calculated to balance the yearly costs of the AHS-

equipped car (AHS) with those of the reference system (REF). The alternative technology

examined here is the AHS. Break even oil costs are related to the break even fuel price as

follows:

CAHS + P’F ∙ FCAHS ∙ D= CREF + P’F ∙ FCREF ∙ D

where: CAHS : Yearly costs for the AHS-equipped car [€/year]

CREF : Yearly costs for the reference system [€/year]

P’F : Break even fuel price [€/lt]

FCAHS : Fuel consumption with the AHS-equipped car [lt/km]

FCREF : Fuel consumption with the reference system [lt/km]

D : Annual distance traveled [km/year]

At this stage it has been assumed that fuel prices are not subject to change over time. The

yearly costs for the reference and the AHS-equipped car are shown in Section 0. Yearly costs

for the alternative technology are estimated to be 3,785 €/year, while yearly costs for the

reference system are 3,570 €/year (see Table 8). The fuel consumption for the reference car is

given in Table 1 (equal to 6.2 lt/100km). For the AHS-equipped car, there is a 20% reduction in

fuel consumption (see also Section 5.2) resulting in 5 lt/100km. We assume that the average

number of km traveled annually is 15,000. About 35% of these trips are inter-urban and are

carried out on highways. An additional 15% corresponds to trips within urban areas over three

lane freeways which can accommodate AHS equipped cars. Based on these rough estimates

which greatly vary between countries, we assume that 50% of the annual mileage is

potentially done on roads over which AHS will be implemented. According to the above, break

even fuel price (P’F) is estimated to be about 2.39 €/lt.

Break even oil price is calculated using the following equation:

Oil Price = Fuel Price - Refining Costs - Distribution Costs - Taxes

Deliverable D6.1 25

where fuel price is the break even fuel price computed above. According to TOSCA WP4

Report [62], distribution costs are equal to 0.4 €/GJfuel. Refining costs and taxes are assumed to

be 3.7 €/GJfuel and 27.7 €/GJfuel respectively.

Considering that the Net Energy Content of the gasoline is equal to 32.2 MJ/lt = 0.0322 GJ/lt

break even fuel price is equal to 74,2 €/GJfuel. Therefore, the break even oil price is equal to:

Oil Price = 74.2 - 3.7 - 0.4 - 27.7 = 42,4 €/GJfuel

or 315 US dollars per barrel (1 Euro=1.3 US$).

5.7 CO2 Mitigation Costs

In this section, the costs to reduce 1 gram of CO2 emissions are calculated. The CO2 mitigation

costs are related to the yearly costs and the CO2 emissions for the reference system and the

AHS-equipped car. They are expressed by the following equation:

D)COCO(

CCC

)AHS(2)REF(2

REFAHS2CO

⋅=

-

-

where: CCO2 : CO2 mitigation costs [€/gr]

CAHS : Yearly costs for the alternative technology [€/year]

CREF : Yearly costs for the reference system [€/year]

CO2 (REF) : CO2 emissions generated by the reference car [gr/km]

CO2 (AHS) : CO2 emissions generated by alternative technology [gr/km]

D : Annual distance traveled [km/year]

The yearly costs for the reference and the AHS-equipped car have been estimated in Section 0

(see Table 8). CO2 emissions for the reference car are equal to 190 gr/km (Table 1). The AHS-

equipped car achieves a reduction of 20% (152 gr/km) in fuel consumption and CO2 emissions

as reported in Section 5.2. The above computation results in CO2 mitigation costs of about 7,55

∙ 10-4 €/gr (or 755 €/tn).

6 Conclusions

This chapter provides a techno-economic analysis that identifies some of the main issues

related to the deployment of Intelligent Transport Systems (ITS) in road passenger transport.

Driver Assistance Systems (DAS) and Automated Highway System (AHS) are evaluated. The

main focus of these technologies is infrastructure capacity improvement, but CO2 emissions

reduction is assessed as well. DAS include ITS technologies that support drivers in maintaining

a safe speed and distance, driving within the lane to avoid overtaking in critical situations. The

concept of AHS requires the use of vehicle sensing systems, inter-vehicle communication

systems, infrastructure sensing systems, and “cooperation” between vehicles and

infrastructure. DAS, AHS and more generally ITS applications hold good promise to provide

significant benefits: they would increase safety, improve the operational performance of the

Deliverable D6.1 26

transportation network, particularly by reducing congestion, reduce emissions and expand

economic and employment growth.

DAS are already present in the form of driver information systems providing guidance,

warnings and alerts to drivers. The next generation of DAS will enable automatic responses to

warnings. The full potential of DAS will become a reality only with large-scale deployment in

vehicles. This requires further regulatory actions. The AHS is expected to emerge from the

development of cooperative vehicle infrastructure (CVIS) platforms. The vision of “driverless”

vehicles moved under autonomous control is not expected to materialize prior to 2030.

AHS is the most promising technology for increasing capacity. This is achieved without having

to build new roads, by introducing “intelligence” in both the vehicles and the roadside. AHS

could also contribute to emissions reduction. However, very high benefits (both with respect

to capacity and emissions reduction) are achieved only at congested areas of application.

The majority of the technical issues regarding automated vehicle operation have been solved

to a great extent. However, there are several issues that need to be considered like

interoperability, standardization and data protection. In an advanced system such as AHS, the

driver will be confronted with significantly more information and possibly more controls than

those currently used in vehicles. The issue of drivers accepting devices which either tell them

what to do or take control away from them is nontrivial. Furthermore, distances between

vehicles are severely reduced at high speeds using longitudinal control and thus drivers may

feel unsafe in the automated vehicle. Societal and institutional issues such as liability are not

fully addressed yet and could delay AHS deployment.

A wide range of ITS technologies are currently at various stages of development but their

eventual widespread adoption will depend on acceptance by both consumers and

governments. Consumers may be willing to pay for AHS services on congested networks and

particularly on motorways with high demand levels where operating in platoons results in

significant time savings. However, there should be a regulatory framework for the introduction

of ITS applications in order to achieve their EU-wide implementation. Leaving the situation

unchanged would lead to stagnation or even deterioration of the current conditions regarding

the deployment of ITS, resulting in an unchanged low level of market take-up and making it

hard to achieve key policy objectives and, indirectly, to contribute to congestion reduction,

road safety and environmental nuisance. The responsibilities of the different players (EU,

public authorities, industry, etc) need to be clearly identified, while business cases including

public-private cooperation should be defined and a legal basis for actions established.

Uncertainties about legal responsibilities and consequences for regulatory regimes may

hamper mass development and market introduction of these systems in Europe.

Table 12 summarizes the main ITS technologies analyzed in this report and their key

dimensions.

Deliverable D6.1 27

Table 12 Most Promising Technology Scenarios

Reference System DAS AHS

Market readiness 2010 2015 2030 Technological

Feasibility R&D

requirements Insignificant Substantial

Capacity 1800 veh/h/l 2000-2600 veh/h/l 4300-6400 veh/h/l Technology

Characteristics GHG emissions

reduction 5% 20%

Retail Price 16,500 € 17,550 € Cost

Characteristics Yearly Costs 3,570 €/year 3,785 €/year

Social equity

implications - to 0 - -

Generation of

jobs within EC + ++

Passenger Safety + +

Privacy 0 - -

Social

Acceptability

Liability - -

User

Acceptability At given user cost 4 4

Cost effectiveness

analysis (Break

even oil price)

42,4 €/GJfuel (315

US$ per barrel)

CO2 mitigation

costs 755 €/tn CO2

Deliverable D6.1 28

List of Tables

Table 1 Reference System Characteristics .............................................................................. 4

Table 2 Introduction of DAS applications in the EU-market ................................................... 6

Table 3 Technological Feasibility........................................................................................... 12

Table 4 Technology Characteristics....................................................................................... 16

Table 5 Unit costs and retail prices for DAS applications ..................................................... 17

Table 6 Unit costs and maintenance costs for AHS elements in passenger cars.................. 18

Table 7 Operating costs after AHS implementation ............................................................. 19

Table 8 Retail price and yearly costs of the AHS-equipped car ............................................ 19

Table 9 DAS retail prices in 2010 and 2020 .......................................................................... 21

Table 10 Social Acceptability of DAS and AHS..................................................................... 23

Table 11 User Acceptability at given User Cost................................................................... 24

Table 12 Most Promising Technology Scenarios ................................................................. 27

Table 13 Overview of DAS penetration rates in premium cars in Europe........................... 38

Table 14 Penetration rates of DAS applications in Europe according to eIMPACT............. 38

List of Figures

Figure 1 Configuration of an eight-car platoon [26] ................................................................ 8

Figure 2 Technology trajectories for road passenger transport .............................................. 9

Figure 3 Simulated road capacity for AHS operation [41]...................................................... 14

Figure 4 Lane capacity in relationship with speed and platoon size...................................... 15

Figure 5 Trends in retail price of ITS elements over cumulative output................................ 20

Figure 6 Trends in yearly costs of an AHS-equipped vehicle.................................................. 21

Figure 7 Speed-flow relationship for a highway under ideal conditions [1] .......................... 35

Figure 8 Development of the TEN-T roads in EU-27 in 2005, 2013 and 2020........................ 35

Figure 9 Assessment of number of lanes for the TEN-T road network.................................. 36

Deliverable D6.1 29

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Road, Application Areas: Vehicle Safety Systems. Retrieved from:

http://ec.europa.eu/transport/its/road/application_areas/vehicle_safety_systems_en.

htm

Deliverable D6.1 30

16. Commission of the European Communities (2008). Action Plan for the Deployment of

Intelligent Transport Systems in Europe and the Proposal for a Directive of the

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Deliverable D6.1 31

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Deliverable D6.1 32

http://www.itsbenefits.its.dot.gov/its/benecost.nsf/ID/80E55CF5EC701787852569610

051E267?OpenDocument&Query=Home

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%20Phase%201%20Report%20%28Final%29.pdf.

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Opportunities for Managing Transportation Performance through Technology.

Available on http://www.its.dot.gov/index.htm

Deliverable D6.1 33

57. Van der Heijden R., Van Wees K. (2001). Introducing Advanced Driver Assistance

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747.

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62. TOSCA (2010). Life cycle assessment of transportation fuels. Deliverable D5, WP4

report, DBFZ, Leipzig.

Deliverable D6.1 34

Annex A

A1: Road Capacity Assessment

In this section, we evaluate the existing road infrastructure in EU-27, focusing on the Trans-

European (TEN-T) road network. The term “capacity” is used to quantify the traffic-carrying

ability of transportation facilities. In this report the definitions are mainly based on the

Highway Capacity Manual [1], the source most often used to estimate capacity. The capacity of

a facility is “the maximum hourly rate at which persons or vehicles reasonably can be expected

to traverse a point or a uniform section of a lane or roadway during a given time period, under

prevailing roadway, traffic and control conditions” [1]. Traffic flow on a highway is the product

of speed and density as given in the following equation [1]:

s(V)L

V1000Q

+⋅=

where: Q : Traffic flow [veh/h/l]

V : Speed [km/h]

L : Vehicle length [m/veh]

s(V) : Separation as a function of speed V [m/veh]

By definition, capacity is the maximum value of Q. The relationship between spacing and

speed (or the so-called “separation policy”) is decided by human driving habits. Under ideal

traffic and geometric conditions, capacities can be as high as 2,400 veh/h/l (see Figure 7). This

capacity is achieved on highways with free-flow speeds of 120 km/h or greater. In practice,

this capacity cannot be achieved due to the difference between the prevailing conditions and

the ideal ones. For example, the theoretical capacity depends on the separation and speed of

vehicles if they continue along with no disturbance. In reality, there will be disturbance

between the vehicles in the highway (when entering or leaving a lane), resulting in lower

capacities [2].

As indicated in Figure 7, the point at which an increase in flow rate begins to affect the

average passenger car speed varies from 1,300 to 1,750 veh/h/l. For highways with free-flow

speeds of 90 km/h, speed will decline when capacity reaches 1,750 veh/h/l. For higher free

flow speeds, the average speed begins to decrease at lower flow rates. Plots for any other

value of speed can be obtained by interpolation.

Deliverable D6.1 35

Figure 7 Speed-flow relationship for a highway under ideal conditions [1]

The TEN-T road network comprises motorways and high-quality roads, whether existing, new

or to be developed which:

• play an important role in long-distance traffic, or

• bypass the main urban centres on the routes identified by the network, or

• provide interconnection with other modes of transport, or

• link landlocked and peripheral regions to central regions of the Community.

The total length of the TEN-T roads including motorways and high-quality roads (but not

including ordinary roads) in the EU-27 in 2005 was approximately 70,200 km. According to

some estimates, the total length of the TEN-T roads will be about 85,000 km in 2013 and about

90,500 km in 2020 [3, 4]. These estimates are presented in Figure 8.

0

20000

40000

60000

80000

100000

120000

Len

gth

(km

) J

Total 98505 99888 100346

Motorway 48186 59201 63125

High quality road 22002 25683 27375

Ordinary road 28317 1500 9845

2005 2013 2020

Figure 8 Development of the TEN-T roads in EU-27 in 2005, 2013 and 2020

Deliverable D6.1 36

The characteristics of TEN-T roads, including the number of lanes and length, were collected

from various sources. Figure 9 presents the number of lanes of the European road network.

More than 80% of the network include highways with three or more lanes (per direction) and

can accommodate more than 5400 or more vehicles per hour per direction. Also, 6% and 14%

of the TEN-T network can accommodate up to 1800 to 3600 vehicles per hour per direction

respectively. Capacity data for specific roads are not generally available, so the definition of

highway maximum capacity is adopted (1,800 veh/h/l). This value provides a first order

approximation of road infrastructure capacity in Europe. The high percentage of highways with

more than 3 lanes (approximately 80%), indicates that European highways can accommodate

AHS systems by dedicating one lane to platoon operations.

Figure 9 Assessment of number of lanes for the TEN-T road network

Deliverable D6.1 37

A2: Deployment status of ITS in Europe and around the world

− Electonic Stability Control (ESC)

ESC has been on the market since 1999 and is standard equipment in many cars of the middle

and upper price classes, but not yet in smaller cars. Sweden has been foremost in the national

promotion of ESC and in 2006 over 90% of new cars sold in Sweden were fitted with ESC [5].

−−−− eCall

A road map for eCall deployment has been established and agreed by the eSafety Forum.

eSafety partners (European Commission, industry, public authorities and other stakeholders)

have agreed to introduce eCall as standard equipment in all vehicles entering the market after

September 2010 (i.e. models of the year 2011) [5]. eCall was initiated by the private sector

(automobile industry) but relies on wider cooperation among key stakeholders for real

operational use. Ongoing discussions on the overall service chain, the institutional framework

(who will handle the incoming messages), the overall benefits and the related necessary

investments (who will/ should pay for safety) are seriously affecting the initial timeline [6].

− Intelligent Speed Adaptation (ISA)

In recent years, ISA technology has spread around the world. Different tests using informative

and supportive systems across Europe have shown that approximately 60-75% of users would

accept ISA in their own cars [5]. Several car manufacturers have installed as standard outfit in

some of their car types manual ISA, i.e. the driver can set a desired speed limit. Then driving

faster than this speed is not possible, except when kick-down is applied [7]. While there is

considerable public support for ISA, an implementation strategy is needed to speed up the

process of implementation of ISA in vehicles. Further harmonisation activities are needed at

international level. Uptake has been slow due to missing road data or incomplete periodic

upgrade of speed limits displayed. In some countries it is very difficult to gather all data from

the different administration layers (towns, regions, departments, national roads etc). The

system is applied in Sweden to all state-owned vehicles, and provided as an option in the

higher end of the market. It is operational on a few parts of the road network, in some other

EU countries [6].

− Collision Avoidance Systems (CAS)

Research on collision warning and collision avoidance systems is taking place in Japan, the

United States and in the European Union through the eSafety programme.

DAS applications have been first introduced, and will continue to do so, in the luxury segment

before trickling down to the mass market. Table 13 shows the penetration rate for some DAS

applications in the European premium car fleet [8].

Deliverable D6.1 38

Table 13 Overview of DAS penetration rates in premium cars in Europe

DAS Application

Penetration rate in

premium cars in

Europe by 2008

Comments

Anti Blocking System (ABS) 100% Already standard today

Adaptive Cruise Control (ACC) 50-60% Prerequisite for further assistance

systems (e.g. Stop & Go)

Lane Departure Warning (LDW) 10-20% Steering interference also possible in

expansion stage

Lane Change Assistant (LCA) 5-10% Requires powerful sensors (24 GHz)

Table 14 shows the penetration rates for some DAS applications that have been assessed by

the eIMPACT Project for 2010 and 2020 [9].

Table 14 Penetration rates of DAS applications in Europe according to eIMPACT

DAS Application Penetration Rate in Europe

2010 2020

Electronic Stability Control (ESC) 25-30% 58-75%

eCall 0.1-0.3% 35-50%

Adaptive Cruise Control (ACC) 0.01-0.1% 3.6-11%

Lane Departure Warning (LDW) 0.9-2.2% 6-18%

Lane Change Assistant (LCA) 0.2-0.7% 3.3-9%

Intelligent Speed Adaptation (ISA) 2-3% 25-39%

References

1. Transportation Research Board (TRB) (2000). Highway Capacity Manual Washington,

DC, USA: National Research Council, 2000.

2. Featherstone C., Lowson M. (2004). Viability and Benefits of Platooning in Automated

Transport Systems. CyberCars Project.

3. European Commission (2010). Mobility and Transport, TEN-T/Transport infrastructure,

TEN-T Components, Road. Retrieved September 12, 2010 from:

http://ec.europa.eu/transport/infrastructure/networks_eu/road_en.htm

Deliverable D6.1 39

4. European Commission (2010). Mobility and Transport, TEN-T/Transport infrastructure,

TEN-T Components, Length of TEN-T roads per country and link type. Retrieved

September 12, 2010 from:

http://ec.europa.eu/transport/infrastructure/networks_eu/doc/rail_tab1_country_len

gth_ten-t_rail.pdf

5. SafetyNet (2009). eSafety-web text. Retrieved 16/10/2009. Available on:

http://ec.europa.eu/transport/road_safety/specialist/knowledge/pdf/esafety.pdf

6. Commission of the European Communities (2008). Action Plan for the Deployment of

Intelligent Transport Systems in Europe and the Proposal for a Directive of the

European Parliament and of the Council. SEC(2008) 3083, Brussels.

7. ADVISORS Project (2002). Inventory of ADAS and User Needs. Deliverable D1.2 v12.3.

8. http://www.oliverwyman.com/ow/pdf_files/20070709_AutoSafetyTechnology_c.pdf

9. eIMPACT (2008). Socio-economic Impact Assessment of Stand-alone and Co-operative

Intelligent Vehicle Safety Systems (IVSS) in Europe. Impact Assessment of Intelligent

Vehicle Safety Systems. Deliverable D4.

Deliverable D6.1 40

Annex B

B1: Questionnaires

Within WP5, a questionnaire was designed and given to experts. The objective was to obtain

the opinion of experts on several issues regarding Intelligent Transport Systems such as their

impact on capacity, energy use, GHG emissions as well as on social and economical issues.

The questionnaire is presented below.

Questionnaire

for EC-FP7 Project:

Technology Options and Strategies Toward Climate-Friendly Transport (TOSCA)

The purpose of this questionnaire is to quantify the uncertainty associated with techno-

economic characteristics of future low-greenhouse gas emission transportation technologies

and fuels, which are being evaluated in the EC FP7-funded TOSCA project. These estimates will

then be used in a scenario analysis for reducing transport sector greenhouse gas emissions.

The questionnaire examines the Intelligent Transport Systems (ITS) that could be implemented

in the European road infrastructure and vehicle fleet over the next 40 years for passenger

traffic.

Although we would be grateful if you indicated your name, your response will be treated

confidentially and will not be revealed to outside the TOSCA project. For information about

the TOSCA project, please visit our website http://www.toscaproject.org/ or contact:

Voula Psaraki

Assistant Professor

National Technical University of Athens

E-mail address: vpsaraki@civil.ntua.gr

Deliverable D6.1 41

Automated Highway System (AHS)-Platoons

The Automated Highway System (AHS) is a concept where vehicles organize themselves into platoons

and are linked together in communication networks, which allow the continuous exchange of

information about speed, acceleration, braking and obstacles. The platoons operate in dedicated lanes.

The platoon vehicles are equipped with longitudinal and lateral control system, advanced steering and

cruise control and systems needed for platoon operations.

1. When do you think the AHS is expected to enter the European market (market readiness)?

.........................................................................................................................................................

2. Do you think that platoons will improve the road capacity? If yes, how much in comparison with

the current capacity of a highway (1800-2400 vehicles/hour/lane)?

........................................................................................................................................................

3. Do you think that platoons will contribute to CO2 emission reduction? If yes, how

much?..............................................................................................................................................

4. How much do you think that the additional retail price of the equipment in the passenger

vehicle will be to ensure platoon operations?……………………………………………………………………………..

5. How much do you think that the additional operating costs of the passenger vehicle will be

when operating in a platoon?...........................................................................................................

6. Do you think that passenger cars will pay tolls in order to operate in

platoons?..........................................................................................................................................

7. In your opinion, what are the R&D requirements for platoon development in Europe? Please

choose from: insignificant, significant (company level), substantial (EU-wide

program)……………….........................................................................................................................

8. Do you believe that there are issues of social acceptability such as the following? Please rate

from -- (unacceptable adverse effect) to ++ (significant benefits); 0 being “comparable to

existing systems”.

a. Social equity implications…………………….....................................................................................

b. Generation of jobs within EC……………………………………………………………………..............................

c. Passengers Safety…………………………………………………………………………………………….......................

d. Privacy issues …………………………………………………………………………………….....................................

e. Driving Comfort and Acceptance……………………………….............................................................

f. Liability issues………….………............……......…....………............………............……….........................

g. Other user acceptability (driverless vehicles)…………………….……............…................... ............

10. Do you have any other comments on the issues related to AHS?

…………………………………………………………………………………………………………………………………………............

Deliverable D6.1 42

B2: Questionnaire Results

The next diagrams show the answers of the experts on issues related to the AHS. On the

whole, 19 experts participated in the survey either by direct interview or by mail. However, as

experts did not address all issues, the number of responses varies from one question to

another. Experts include participants in EU-funded projects, academics, experienced

transportation engineers etc.

Question 1: When do you think the AHS is expected to enter the European market (market

readiness)?

4

9

2 3

0

2

4

6

8

10

Frequ

ency

.

2025 2030-2040 2050 maybe never

Market Readiness of AHS

N=18

Question 2: Do you think that platoons will improve the road capacity? If yes, how much in

comparison with the current capacity of a highway (1800-2400 vehicles/hour/lane)?

3

5

4

3

0

1

2

3

4

5

Fre

quency

.

10-20 % 21-30% 50-80% 300%

Capacity Improvement

Road Capacity Improvement by AHS

N=15

Deliverable D6.1 43

Question 3: Do you think that platoons will contribute to fuel consumption reduction? If yes,

how much?

7

8

10

1

2

3

4

5

6

7

8

Fre

quency

.

5-10% 15-25% No effect

CO2 Emissions Reduction

CO2 Emissions Reduction by AHS

N=16

Question 4: How much do you think that the additional retail price of the equipment in the

passenger vehicle will be to ensure platoon operations?

1 1

13

2

0

2

4

6

8

10

12

14

Fre

quency

.

0 € 500 € 1000-2000 € 4000-5000 €

Additional Retail Price

Additional Retail Price of the AHS-equipped car

N=17

Question 5: How much do you think that the additional operating costs of the passenger

vehicle will be when operating in a platoon?

5

8

1

0

1

2

3

4

5

6

7

8

Fre

quency

.

0 €/year 50-200 €/year 500 €/year

Additional Operating Costs

Additional Operating Costs of the AHS-equipped car

N=14

Deliverable D6.1 44

Question 6: In your opinion, what are the R&D requirements for platoon development in

Europe? Please choose from: insignificant, significant (company level), substantial (EU-wide program).

2

14

0

2

4

6

8

10

12

14

Fre

quency

.

Insignificant Significant Substantial

R&D Requirements for AHS

N=16

Question 7: Do you believe that there are issues of social acceptability such as the following?

Please rate from -- (unacceptable adverse effect) to ++ (significant benefits); 0 being “comparable to

existing systems”.

1

7

6

1 1

0

1

2

3

4

5

6

7

Fre

qu

ency

.

Unac

cep

tab

le

eff

ect

(- -)

Ad

vers

e

eff

ect

(-)

Co

mpar

able

to r

efe

ren

ce

(0)

Signif

ican

t

be

ne

fits

(+)

Ve

ry

signif

ican

t

be

nefi

ts (

++)

Social Equity Implications for AHS

N=16

5

7

4

0

1

2

3

4

5

6

7

Fre

quency

.

Unac

cep

tab

le

eff

ect

(- -)

Ad

vers

e

eff

ect

(-)

Co

mp

arab

le

to r

efe

ren

ce

(0)

Sign

ific

ant

be

ne

fits

(+

)

Ve

ry

sign

ific

ant

be

ne

fits

(+

+)

Generation of jobs within EC by AHS

N=16

3

7

6

0

1

2

3

4

5

6

7

Freq

uen

cy

.

Unacc

epta

ble

effect

(- -)

Advers

e

effe

ct (-)

Com

par

able

to r

efe

rence

(0)

Signific

ant

be

ne

fits

(+)

Ve

ry

signif

ican

t

ben

efi

ts (+

+)

Passengers Safety by AHS

N=16

3

4

9

0

1

2

3

4

5

6

7

8

9

Fre

quency

.

Un

acce

pta

ble

effe

ct (-

-)

Adve

rse

effe

ct (-)

Co

mp

arab

le

to r

efe

ren

ce

(0)

Sign

ific

ant

ben

efits

(+

)

Ve

ry

sign

ific

ant

ben

efits

(++

)

Privacy Issues for AHS

N=16

Deliverable D6.1 45

12

10

3

0

1

2

3

4

5

6

7

8

9

10

Freq

uency

.

Unacce

pta

ble

eff

ect

(- -)

Advers

e

effe

ct (

-)

Co

mp

arable

to r

efe

ren

ce

(0)

Sig

nific

ant

ben

efi

ts (

+)

Very

sign

ific

ant

be

ne

fits

(+

+)

Driving Comfort and Acceptance for AHS

N=16