Newsletter Integrated Human Modelling and Simulation to support Human Error Risk Analysis of Partially Autonomous Driver Assistance Systems

August 2011

IN THIS ISSUE:Approaches to driver modelling in ISi-PADASOFFIS / IFSTTAR / CRF Modelling ApproachSUPELEC / KITE Modelling ApproachInteraction Driver Models - JDVE - RBD ToolCase Studies

The aim of ISi-PADAS is to support the design process of driver assistance systems. The focus of the project is to provide a Risk Based Design (RBD) methodology for the estimation of risks associated with the introduction of a new assistance system. Within WP2, driver models are evaluated as means to support different aspects of the methodology and the PADAS design.

Five teams are conducting work in WP2, each on a dedicated driver model.

The OFFIS team developed a model able to be executed within a driving simulator. This model is used to simulate drivers’ behaviour with and without assistance. Based on these simulations, the risk associated with a situation can be assessed with and without an assistance system.

Another driver model executable in a driving simulator was developed by the IFSTTAR team. It aims at identi-fying relevant scenarios which the RBD methodology should focus on. It takes limitations of drivers’ cognitive system into account and is thus able to simulate also erroneous behaviour.

CRF developed a model able to detect distracted states of the driver. This model aims to support the strategy of the assistance system by providing additional information about the driver.

The approach developed by the SUPELEC team is used to estimate the probability of extreme behaviour of drivers. As such behaviour occurs rarely, Monte-Carlo approaches are not sufficient. Therefore SUPELEC utilises an approach based on

Extreme Value Theory.

The team at KITE developed a taxonomy of human errors for the classification of the errors that a driver makes in interact-ing with a machine and associ-ated context or environment. This taxonomy is used by all driver models to share a common classification of possible human errors.

A useful discrimination of the different modelling approaches can be made:

•sssssssssThere are two simulation models: the IFSTTAR and the OFFIS model. They are able to generate realistic driving behav-iour within a driving simulator.

•sssssssssThere are two models for analysis: the CRF and the SUPELEC model. These models are able to analyse the behav-iour of drivers or driver models.

•sssssssssKITE’s taxonomy provides a meta-model which can be

used to categorise the predic-tions of the other models.

WP2 is strongly connected to the other WPs of the Project (Figure 1). The development of the models is based on empirical data provided by driving experiments conducted in WP1. The CRF model is used within the assistance system developed in WP3. The simulation models are executed within a Joint Driver-Vehicle-Environment (JDVE) Platform developed in WP4. Finally the simulation models and the SUPELEC approach support the risk quantification for the RBD methodology from WP5.

In the next pages, this News-letter will provide a detailed description of each driver model and will present the connection between them and the RBD methodology, which was described in Newsletter 4.

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The research leading to these results has received funding from the European Commission’s Seventh Framework

Programme (FP7/2007-2013) under grant agreement n°218552 Project ISi-PADAS.

Approaches to driver modelling in ISi-PADAS

Figure 1: ISi-PADAS Project Schema

Relevant Publications

1

2-3-4

5-6

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8-9

HMAT Conference Prooceedings - Book:

Title: Human Modelling in Assisted Transportation

Editors: Pietro Carlo Cacciabue, Magnus Hjälmdahl, Andreas Luedtke

and Costanza RiccioliPublisher: Springer

Information on book sale: info@kitesolutions.it

Created by

Final Event at page 10

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The OFFIS driver model substitutes human drivers. It is executable within a simulation platform and able to drive a simulated car.

The model is made up of two parts. One part consists of domain-dependent knowledge. It represents the knowledge required to drive a car. The other part is the cognitive architecture CASCaS (Cognitive Architecture for Safety Critical Task Simulation). This architecture is able to interpret the driving knowledge

OFFIS Modelling Approach

Figure 2: Overview on CASCaS components

(Wickens, C.D. & McCarley, J.S. 2008. Applied Attention Theory. CRC Press, Boca Raton) model.

P(AOI) = S – Ef + Ex • V

More relevant for the risk assess-ment of the assistance systems developed in ISi-PADAS is the work on longitudinal control models.

Diego) models has been modified to a hierarchical modular proba-bilistic architecture, as shown in Figure 3.

The driver model has been constructed by decomposing complex manoeuvres into basic behaviours and vice versa:

and apply it to the current situation within the simulator (Figure 2).

To support the RBD methodology of ISi-PADAS, the modelling work conducted at OFFIS focused on develop-ing a driver model, which is not only able to generate realistic driver behaviour, but also shows a comparable variance in behaviour as human drivers do.

For this purpose, OFFIS focuses on two aspects of drivers’ behaviour:

• Modelling drivers gaze movement behaviour including probabilistic elements.

Figure 3: Hierarchical structure of the BAD MoB model

A set of hierar-c h i c a l o r g a n i z e d b e h a v i o r models have been created in order to model the complex competence of human drivers in the scenario chosen as use case for the demonstration of the RBD methodology.

Figure 4: Comparison of speeding profiles at traffic light approaches between human and model drivers, using no assistance (a) and using a FCW+ system (b)

• Modelling drivers longitudinal control b e h a v i o u r (Acceleration / Decel-eration) including a realistic variance in behaviour.

Concerning the gaze behaviour, the mecha-nisms of CASCaS have been extended by a component that controls the attention of the model based on the top-down elements of Christopher Wickens well known SEEV

Bayesian Autonomous Driver Mixture-of-Behaviours (BAD MoB) models.

The model behaviour has been validated against human behav-iour obtained from empirical driver studies conducted in driving simulator within WP1.

It could be shown that the model showed a very similar behaviour including similar variance in behaviour (see e.g. Figure 4).

For the estimation of probabili-ties associated to specific critical behaviour, the model has been simulated 10.000 times in a selected driving scenario without the assistance system.

The same procedure has been done with a FCW+ system installed in the JDVE.

The simple architecture of BAD (Bayesian Autonomous Driver) (Möbus, C. & Eilers, M. (2009). Further steps Towards Driver Modeling according to the Bayesian Programming Approach, In Duffy V.. (ed.) Digital Human Modeling, pp. 413-422,LNCS (LNAI), Springer, San

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IFSTTAR has developed a Cognitive Simulation Model of the driver in order to simulate driving activity at three main different levels:

• Perception of the road environ-ment;

• Cognition including Mental Repre-sentations elaboration (corresponding to the driver’s Situational Awareness) and Decision Making abilities (based on a Risk Matrix conflict associ-ated with anticipated SA changes in the near future, assessed through cognitive simulation abilities); and

• Executives Functions in order to implement action on a virtual car and to dynamically progress in a virtual road environment, through an iterative and adaptive “Perception-Cognition-Action” regulation loop.

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Figure 5: Driving activity as a Perception-Cognition-Action regulation loop

IFSTTAR Modelling Approach

By contrast with the partially deter-ministic solution of pre-defined visual scanning patterns (modelling as a sequence of more or less long fixation points) used in initial version of the perception module, the Visual Strategy Manager is able to dynamically manage perceptive queries (request for information to be obtained) continuously coming from the different processes that are active at a given time.

Each query will require focusing the virtual eye on a specific area of the road scene. Then, perceived information is integrated into the mental representation of the Cognition Module.

The IFSTTAR model was success-fully able to demonstrate the effect of driving under distraction, by simulating drivers in normal and in distracted conditions.

opment of a cognitive function of mental deployment of driving schemas, simulating drivers’ abilities to project themselves in the future through mental simulations.

By using driving schemas, this deployment consists in “mentally moving” the vehicle along a driving path, by successively progressing along the different driving zones of the schema, from the initial state until reaching the tactical goal. For decision-making, COSMODRIVE can successively implement several mental deployments, corresponding to different executions of the same

IFSTTAR modelling approach is based on a pre-existing theoretical model so-called COSMODRIVE (COgnitive Simulation MOdel of the DRIVEr) dedicated to driver’s mental activities simulation.

The key-component of COSMODRIVE is the drivers’ mental representation of the driving environment, corre-sponding to the driver’s Situation Awareness (SA).

During ISi-PADAS the functional architecture of the Perception Module was extended in order to simulate human visual scanning in a more realistic way. In this architecture (see Figure 5), perceptive processes of informational integration and perceptive exploration are under the control of a key mechanism: the Visual Strategies Manager (Figure 6). Figure 6: Functional architecture of the perception module

Concerning decision-making process, progress has been made in ISi-PADAS concerning the devel-

Figure 7: The virtual eye of COSMODRIVE

driving schema based on alternative actions liable to be implemented in the current context.

The decision-making process is supported by a Risk Conflict Matrix used by the model for assessing the collision risk between the ego car and other road users.

When a critical conflict is detected between the ego car and another vehicle, the driving schema deploy-ment is cancelled, and a new solution is mentally examined for exploring behavioural alternatives (e.g. braking against accelerating) liable to be implemented at this time.

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CRF has developed a Model for Analysis, with a specific focus on driver’s mental status in terms of distraction risk (visual and cogni-tive). Driver’s distraction has become an important and growing safety concern with the increasing use of the so-called In-Vehicle Infor-mation Systems (IVIS), such as cell-phones, navigation systems, etc. A very promising way to overcome this problem is to detect driver’s distraction and thus to adopt in-vehicle systems accord-ingly, in order to avoid or mitigate the negative effects. The developed models apply three well-known data-mining methods:

• Artificial Neural Networks (ANN);

• Support Vector Machines (SVM);

• Adaptive Networks-based Fuzzy Inference Systems (ANFIS).

Despite of what already done in literature, this method does not use eye-tracker data in the final classi-fier. Data for the models were collected using a static driving simulator, with real human subjects performing a specific secondary task (SURT) while driving.

Potential applications of this research include the design of adap-tive IVIS and of “smarter” Partially Autonomous Driving Assistance Systems (PADAS).

Comparison of Classifiers Performance considering differ-ent Values of mobile Windows

The data collected during the WP1 experiments has been pre-processed by moving a mobile window through the dataset, in order to apply a mobile average to filter the data and so to obtain a smoother profile.

These windows have a maximum size of 300 ms in order to still allow real-time application. A 300ms Window consists of 6 samples (20 Hz recording frequency). The influence of the different average window values of the data on the model performances has been deeply investigated, by comparing the resulting correct detection rate. It could be shown in Table 1 that Window sizes seem to affect the results very few. A window size of 300ms showed only little advantage over other sizes.

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CRF Modelling Approach

Figure 8: Performance comparison of different classifiers (windows: 300 ms)

Comparison of Performance for different types of Classifiers

The three mentioned classifier techniques have been evaluated, whereat for the neural networks two different kinds have been used:

• Feed-forward Neural Networks (FFNN);

• Layer-Recurrent Neural Networks (LRNN).

Evaluation on LRNNs is still ongoing work. The performance results for the other techniques can be found in Figure 8.

The most important conclusion of the investigations is that SVM provides the best results with respect to all the other classifiers used. Therefore, we can say that driver’s distraction classifier based on SVM is the best one to be imple-mented in real-time and to be used to assist drivers.

It is worth noting here the excellent value of CR achieved, in general: for almost all subjects it is over 90%, achieving the highest value for subject 1 (with more than 96% of test-instances correctly classified) and anyway with an average of almost 95% for all subjects. This is perfectly in line with the best results presented in literature and even better if we consider that no eye-tracker device has been used as input to the classifier.

Table 1: Effect of window size on correct detection rate (CR)

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SUPELEC has developed Performance Assessment Models, based on statis-tical learning tools (nonparametric statistics). The aim of this statistical model is to learn the distinction between “normal” and “abnormal” states on the basis of (real or model-generated) driver traces, in order to provide warnings/alarms when a possibly dangerous situation is encountered. The approach is based on Extreme Value Theory and uses experimental data from WP1.

The same statistical tools are also used to estimate event probabilities in the RBD methodology developed in WP5, on the basis of a limited number of traces. Within ISi-PADAS these traces are generated by the OFFIS simulation models of the driver.

There are two approaches to deter-mine a domain of failure. The first one is to decide beforehand the domain of failure based on expert knowledge. The second approach is based on experiments. The objective is to determine a region of the critical performance space that is considered as a central set, in the sense that it contains a very high fraction of the number of samples. On the contrary, the complement of this central set will be considered as the set of extreme values. The determination of this central set can be conducted using classical statistical tools and in particular those concerned with the estimation of multivariate quantiles. This approach will be retained in our work.

SUPELEC working hypothesis is as follows. It is assumed that a risk of collision can be detected by monitor-ing the value X of some function of the observations, and that the risk of collision becomes high if X is higher than a threshold t. The threshold t can be given by an expert or deter-mined empirically from data. Finally, it is assumed that the event {X>t} corresponds to a tail event, which means that the event does not occur frequently.

For the study of limiting behaviour of sample extremes the Generalized Pareto Distribution (GPD) has proven to be important.

The methodology that is followed by the SUPELEC team involves the following steps (see Figure 10):

Step 1: Choose a quantity of interest related to a risk of collision

Step 2: Perform a preliminary data analysis and choose a threshold u above which it is appropriate to use the GPD

Step 3: Estimate the parameter of the GPD

Step 4: Tail predictions (and validation)

The approach has been demon-strated on the use case for the RBD methodology, which consists of a scenario, where a leading vehicle stops in front of a traffic light, and the ego vehicle should stop too. This scenario is simulated using the Joint Driver Vehicle Environment Simulation Platform (JDVE) developed in WP4. The dataset consists of n=10000 runs, produced by the OFFIS driver model.

Events in which the driver brakes very late (time-to-collision when braking starts below 5 seconds) and to faintly (mean acceleration above 1.6 m/s2) have been analyzed with the acceleration value as the chosen quantity of interest (Step 1).

Based on a data analysis utilizing mean access functions (MEF) a threshold of u=-2.05 m/s2 has been chosen (Step 2).

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Figure 9: EVT model compared to data generated by driver model

SUPELEC Modelling Approach

computed (Step 4).

The distribution function can be seen in Figure 9.

The estimated probability is approximately equal to 3*10-4, which is, by chance, the value found by the Monte-Carlo estima-tor.

When changing the event definition by selecting a threshold of -1.5m/s2, the simulation data did not provide a single event, but the EVT approach estimates an event probability of approximately 5.7*10-6.

Figure 10: Functional architecture of the SUPELEC approach

After estimation of the parameters of the GPD distribution (Step 3), tail predictions have been

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August 2011

Table 3: Error Modes per generic Error Type: Incorrectly timed action

taxonomy of human error. It means that:

• Easy to understand: a strong degree of simplicity has to be retained at all time.

KITE’s Meta-Model aims at provid-ing a taxonomy of human errors for the classification of the errors that a driver makes in interacting with a machine and associated context or environment. This taxonomy is utilised by all simulation driver models to share a common classifica-tion of possible human errors and it is also be used in the RBD methodology developed inside WP5.

The model utilised for developing a specific taxonomy for usage within ISi-PADAS is based on the so-called Information Processes System (IPS) paradigm which considers four reference cognitive functions as basic mechanisms of human cognitive and behavioural performances. These functions are: perception, inter-pretation, planning and execu-tion.

With respect to a taxonomy of human error, two concepts are fundamental: error types and error modes.

Error types are generic descriptions of possible erroneous performances both at mental and behavioural level.

Error modes are specific and quanti-fied forms taken by error types in a certain working and operational context. Therefore, error modes are subsets of error types. The basic error modes accounted for in the taxonomy make reference to each cognitive function.

The taxonomy of human error devel-oped in WP2 has the goal to support the driver models which predict human behaviour and HMI. For this it has been designed to be an easy-to-understand, operational, living

• Operational: a strong link with the real environment of driving in the presence of automation and PADAS systems is neces-sary.

• Living: the taxonomy must be flexible, i.e., it must be possible to expand and improve the items of the taxonomy.

The resulting taxonomy developed for WP2 is detailed enough to provide a complete overview of all error modes that can be identified inasso-ciation to the four cognitive functions.

At the same time, as WP5 aims at supporting a new RBD methodology for Human Error Risk Analysis, which is essentially a prospective type of analysis for the numerical evaluation of the likelihood of events, it becomes essential to focus mainly on manifestations of errors rather than on mental processes. Therefore, the taxonomy developed in WP2 relative to the cognitive function “Execution” (Table 2) has been further elabo-rated for the definition of specific error modes relative to error types of the taxonomy.

In practice, for each Generic and Specific Error Type, relative to the cognitive function “Action/Execution” a set of Error Modes has been identi-fied. This has been done in order to enable, eventually, to quantify the associated basic human error probabilities. For example, Table 3 contains a possible set of Error Modes for the error type “Incorrectly timed action” associated to the Cognitive Function “Execution”.

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KITE Modelling Approach

Table 2: Error Types associated to Cognitive Function “Execution”

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Inside ISi-PADAS project, the interac-tion between driver models, JDVE and RBD methodology has been a very important activity. The final objective is to utilise the models for simulating and analysing different scenarios in fast time.

JDVEThe Joint Driver Vehicle Environment (JDVE) Simulation Platform devel-oped in WP4 is a simulation platform which has been used to integrate the different software results of the ISi-PADAS project. It includes basic scenarios used in the experiments of WP1 as well as the two PADAS systems from WP3. For more details about it, please refer to the Newslet-ter 3.

JDVE allows simulation runs with and without PADAS systems, so that real drivers can test the PADAS in real driving simulators. In addition, JDVE main goal is to enable automatic tests of PADAS. Therefore, the driver models COSMODRIVE of IFSSTAR and CASCaS of OFFIS have been attached to the JDVE.

Moreover, an accelerated time testing feature has also been applied to the JDVE which allows running

more tests in time, e.g. performing thousands of simulation runs in short time. This makes it possible to also detect special system behaviour occurring in very few conditions only.

RBD ToolInside ISi-PADAS project, the RBD Tool represents the software imple-mentation of the RBD methodology developed in WP5.

From the point of view of the safety analyst, the most important feature of the RBD Tool is the possibility of supporting the user in the creation, design and evaluation of the Expanded Human Performance Event Trees (EHPETs) and the main result is the final calculation of the risk matrix for each sequence depicted in the tree.

The risk matrix of the RBD Tool is made of three different areas: red zone, yellow zone and green zone. More details about it can be found in the Newsletter 4.

Interaction Driver Models - JDVE - RBD ToolFrom the perspective of the RBD Tool, three possible contributions from driver models and JDVE can be

envisaged:

• They can be utilised for the identification of events associ-ated to driver inadequate performances. In this case, by simulating a specific situation with driver models, it is possible either to understand if the considered events are consis-tent with the overall scenario or to identify new and different branches of the EHPET that were not originally imagined by the analyst.

• They can be utilised for the evaluation of probabilities of driver events (through a Monte-Carlo approach included in the JDVE and/or the EVT model developed by SUPELEC). In this sense, they give a very impor-tant support to classical safety analysis and human error risk analysis techniques.

• They can be used for the evalua-tion of consequences of specific sequences of the EHPET.

From a more generic point of view, interaction between driver models, JDVE and RBD Tool can be described as shown in Figure 11.

Interaction Driver Models- JDVE - RBD Tool

Figure 11: Iterative process between RBD Tool and JDVE

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EHPETsFor the two cases, two different EHPETs have been produced, by utilising the RBD Tool.

In the first case, only driver perfor-mances are present (in total, 9 sequences).

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Case studies

Figure 12: Screenshot of the TL scenario in the JDVE

Inside ISi-PADAS, the RBD method-ology has been demonstrated by utilising the JDVE in a Traffic Light (TL) scenario. It consists of an urban scenario in which the ego vehicle (EV) is following another car (leading vehicle – LV), while approaching the traffic light (Figure 12).

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Two different cases have been studied:

• In the first case, the EV has no assistance systems.

• In the second case, the EV has installed a PADAS system: the Advanced Forward Collision Warning (FCW+) developed inside the project and utilised for the safety distance control. This system gives a warning sound when a critical distance / time headway to an obstacle is reached. If a further level of criticality is reached, then emer-gency braking is performed. Finally, also assisted braking is provided to the driver.

In both cases, the LV stops at the TL; hence, the mission of the EV is to stop beyond the LV.

The final goal of the study is to compare the two cases and to show the safety improvement provided by the introduction of FCW+ from the hazard mitigation perspective.

The second case also includes the PADAS events, which are character-ised by the activation or not of the different functionalities: warning, assisted braking and emergency braking (in total, 51 sequences).

ProbabilitiesDifferent procedures and techniques have been used for the estimation of the probabilities associated to the branches of the EHPETs.

The probabilities associated to PADAS failures have been estimated with “expert judgment”.

With regard to driver performances, in the first case (no PADAS on the EV), the probability of each sequence has been assessed using the results of the simulation runs in the JDVE. Two different approaches have been utilised: the classical Monte-Carlo approach and the EV model from SUPELEC.

In the second case study (PADAS installed on the EV), a mixed approach has been used: JDVE and THERP Technique (described in the Newsletter 4).

In both cases, the results from JDVE have been based on a number of 10000 simulation runs.

ConsequencesA review of the biomechanics of impacts in road accidents makes it clear that there is no simple relation-ship between impact severity and the severity of injuries sustained by road users. The criterion used in this work to define the severity level is based on the speed at which a collision takes places. Hence, in the two case studies, for the simple illustration of the RBD methodology, the levels identified in the Table 4 have been considered.

In the case without PADAS the evalu-ation of the consequences is based on the results of the simulation. For each sequence the worst case is considered, which is determined by considering the maximum value of the speed when crashing occurs.

As for the probabilities assessment, a mixed approach is utilised in the case with PADAS for the evaluation of consequences: JDVE and expert judgement. This was due to the fact that not all sequences can be repro-duced by the simulation platform.

Table 4: Severity levels

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Risk AssessmentIn the RBD Tool, the measures of risk for all sequences of the EHPETs of the two case studies are obtained by the user by combining frequency and consequence estimates through the risk matrix. Two examples of result-ing risk are shown in Figure 13.

The first result (case without PADAS) corresponds to the sequence where the driver brakes very late and not hard enough. The result is situated in the red zone, which is the unaccept-able risk area.

The second result (case with PADAS) of the Figure 13 concerns the sequence in the case where the

driver brakes very late and not hard enough followed by the failures of all PADAS functionalities (warning, assisted braking and emergency braking). This case represents the worst case: bad reaction of the driver and all PADAS barriers fail. It leads to hazardous consequences but its probability is very low. So the sequence is extremely improbable and situation in the acceptable (green) zone.

In general, the demonstration of the RBD methodology showed that all the sequences that contain the sub sequence “the driver brakes very late and not hard enough” in the case without PADAS are situated in the red

zone.

Instead, in the case with PADAS, they are moved in the green zone. This shows the efficiency of the PADAS from safety point of view.

It is due to say that the driver models developed inside the ISi-PADAS project can be considered not exhaustive enough for the purposes of the RBD approach in terms of discovery of driver behaviours as well as of test of the predictions of the risk based design. However the application of the JDVE to the TL scenario shows the potentiality of the RBD methodology and its combined use with the driver models and simulation platform.

Figure 13: Examples of results of the risk matrix in the two case studies

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Final ISi-PADAS

Workshop 30th August 2011

University of Modena and Reggio Emilia

viale Antonio Allegri, 9 – 42121, Reggio Emilia, Italy

E-mail: infows@isi-padas.eu Web site: www.unimore.it

Organised by

Partners of the project

INVITATION Modern vehicles are equipped with an increasing amount of assistance systems, aimed at im-proving safety and the quality of performance. However, they lead to changes in the driving task and in driver behaviour. Currently, the effects of these systems are investigated empirically by performing tests in driving simulators or with prototypes on test tracks. Further effects are exam-ined after market introduction based on field operational tests and accident reports. In order to avoid these expensive and time consuming tests, the ISi-PADAS project provides an innovative methodology to support risk based design of Partially Autonomous Driver Assistance Systems (PADAS) focusing on assessment and mitigation of driver errors by an integrated Driver-Vehicle-Environment modelling approach. This implies the reduction of testing effort through the substitution of real drivers by driver models. The objective of the final ISi-PADAS Workshop is to present models, methods and tools developed within the project and to provide an environment for fruitful exchange of ideas.The main topics are:

Presentation of the main results achieved by the project. Demo sessions with the prototypes of ISI-PADAS installed on simulators. Road-show of the project results and next possible steps. Risk Assessment of Human-Machine Interaction.

The Workshop consists of keynote lectures and presentations from partners of the project. ISi-PADAS partners welcome everybody who would like to participate to the conference and offer to the participants the possibility of driving the simulated car with the PADAS applications installed.

WORKSHOP PROGRAMME 08.30 – 09.00 Welcome and registration Luca Minin 09.00 – 09.15 Introduction to the Event and to ISI-PADAS project Jens Alsen 09.15 – 09.45 Key-note Speaker 1, including discussion:

“Modeling driver's vigilance impairments in monoto-nous environments”

Andry Rakotonirainy (Queensland University of Technology, Australia)

09.45 – 11.15 ISI-PADAS Session 1 – Experimental Session real driving studies studies about driving without PADAS studies about safety effects of PADAS studies about automation effects of PADAS

ISI-PADAS Session 2 – PADAS target applications Ses-sion

PADAS functionality as an MDP, solved with RL approach

Dedicated HMI for PADAS

People from Consortium María Alonso

Martin Bauman

Elke Muhrer

Luca Minin

Fabio Tango Christine Megard

11.15 – 11.45 Coffee Break All 11.45 – 12.45 ISI-PADAS Session 3 – Driver’s Models Session

OFF Modeling Approach INR Modeling Approach CRF Modeling Approach SUP Modeling Approach

People from Consortium Bertram Wortelen Thierry Bellet Fabio Tango Nabil Sadou

13.00 – 14.15 Lunch Break All 14.15 – 14.45 Key-note Speaker 2, including discussion:

“Understanding driver behavior using naturalistic and field operational test data”

Marco Dozza (Chalmers Uni-versity, Gothenburg, Sweden)

14.45 – 15.00 Road-map and exploitation of ISI-PADAS Rainer Heers 15.00 – 17.30 ISI-PADAS Session 4 – Development of Methodologies

and Simulator Presentation of J-DVE simulator Presentation of RBD Tools and Methods

Demo Sessions, where prototypes and tools are availa-ble for testing to participants Coffee break available in the meanwhile

Julian Schindler

Mirella Cassani All delegates

17.30 End of the workshop from ISI-PADAS project All

Integrated Human Modelling and Simulation to support Human Error Risk Analysis of Partially Autonomous Driver Assistance System

August 2011

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Detailed articles will inform you about:• Recent news• Upcoming events• Achieved results

Consortium

Here you can find background material concerning:• Project structure• Partners• Contacts

Jens Alsen - OFFISjens.alsen@offis.de

Project CoordinatorFabio Tango - CRFfabio.tango@crf.it

Technical Project Manager

The research leading to these results has received funding from the European Commission’s Seventh Framework

Programme (FP7/2007-2013) under grant agreement n°218552 Project ISi-PADAS.

Project Officer: Grzegorz Domanski

Contacts

Visit www.isi-padas.eu Read our newsletter

Advisory Board Members of the Advisory Board are:• Prof. Dr. Klaus Bengler - Technische Universität München - Germany• Prof. Dr. Toshiyuki Inagaki - University of Tsukuba - Japan• Dr. Andrew Liu - MIT Man Vehicle Lab - United States of America• Prof. Dr. Josef F. Krems - TU Chemnitz - Germany• Prof. Dr. Andry Rakotonirainy - CARRS-Q, Queensland University of Technology - Australia

DisseminationMirella Cassani - KITmirella.cassani@kitesolutions.it

Newsletter 5

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