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COSMO-SIVIC: a first step towards a virtual platform for Human Centred Design of driving assistances
COSMO-SIVIC: a first step towards a virtual platform for Human Centred Design of driving assistances Bellet T.*, Mayenobe P.*, Bornard J.C.*, Gruyer D.**, Mathern B. *
*Institut National de Recherche sur les Transports et leur Sécurité (LESCOT: Laboratoire Ergonomie et Sciences Cognitives), 69675 Bron - France (Tel: 33 (0)4 72 14 24 57; e-mail: bellet@ inrets.fr) ** Institut National de Recherche sur les Transports et leur Sécurité - Laboratoire Central des Ponts et Chaussées (LIVIC: Laboratoire Interaction Véhicule-Infrastructure-Conducteur), 78000 Versailles-Satory (e-mail: [email protected]) Abstract: This paper presents the first step of research work implemented by INRETS in the frame of the ISi-PADAS European project, in order to develop a simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This HCD tool (called COSMO-SiVIC) integrates a cognitive simulation model of the Driver (called COSMODRIVE) on a virtual VehicleEnvironment platform (SiVIC). From this future tool, it is expected to compare since the earlier stages of the technological design, virtual simulation of driving performances with and without driving assistance, and thus to appreciate the potential benefits, interests and risks of vehicle automation on road safety. Keywords: Driver modelling, Cognitive simulation, Virtual Human Centred Design, Vehicle automation. 1. INTRODUCTION: CONTEXTE AND OBJECTIVES Technological advances in car industry during the last decade have now made possible to design automated systems able to takeover the vehicle in normal driving conditions (e.g. adaptive cruise control) as well as in critical situations (to avoid collisions or lane departure, for example). Such a “revolution" will radically modify the driving activity in the near future in term of vehicle control sharing between the human driver and the automatisms. However, automation past efforts in other areas, like nuclear plants or aviation, have demonstrated potential risks on safety (e.g. the “ironies of automation” of Bainbridge, 1987): automation may be source of specific human errors and may cause critical conflicts between human and machine. To maintain human operator “in the loop” of control is also essential in case of automation malfunction, or when the driving situation comes out of the validity limits of the assistance. Consequently, the decision and the way to use automation in order to assist, or to replace the human driver must not only take into account the technological capabilities themselves, but also the human needs, their own abilities, and their acceptance regarding technological assistances. A Human-Centred design approach of these devices is particularly important in car driving context, by considering the heterogeneity of the drivers, the potential risk of the driving task, the variability of the driving situations and, lastly, the responsibility issue in case of accident. However, integrating end-users needs is not always easy, especially when one wishes to develop innovative devices often costly to develop. Ergonomics evaluations with real drivers indeed required to develop mock-ups and operational prototypes which are generally expensive, and may cost a lot of time. In order to better promote the human drivers’ needs, characteristics and abilities since the early stages of technological design, a new generation of virtual tools is necessary. Such virtual tools should jointly include models of (i) the Driver, (ii) the Vehicle, and (iii) the road
Environment (i.e. DVE platform), in order to virtually assess the driving assistance effects through simulation. That is the global objective of the ISi-PADAS European project (Integrated human modelling and Simulation to support human error risk analysis of Partially Autonomous Driver Assistance Systems). The research presented in this paper takes place in this Human Centred Design approach, aiming at setting up a virtual simulation platform to design and evaluate in-vehicle systems interest and potential impact on road safety. In this objective, it is proposed as a first step to implement a cognitive simulation model of the Driver (called COSMODRIVE) on a Vehicle-Environment platform (called SiVIC), in order to provide a DVE simulation platform, COSMO-SiVIC, liable to .support in the future the virtual design of vehicle automation technologies. 2. COSMODRIVE: A COGNITIVE SIMULATION MODEL OF THE DRIVER COSMODRIVE is a COgnitive Simulation MOdel of the DRIVEr developed at INRETS-LESCOT in order to simulate all the driver’s mental activities carried out while driving, from perceptive functions to behavioural performances (Bellet, 1998; Bellet et al., 2007). SITUATION AWARENESS
PERCEPTION COGNITION
DECISION MAKING MENTAL REPRESENTATION
Perceptive Cycle ACTION PLANNING
COGNITION
PERCEPTION
Regulation Loop ACTION
ACTION IMPLEMENTATION (Executive Functions)
Fig. 1: Driving activity modelling in COSMODRIVE
Synthetically, the heart of COSMODRIVE theoretical model are the drivers’ mental representations of the driving environment, corresponding to the driver’s Situation Awareness, according to Endsley (1995) definition of this concept: the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. This mental models are built in working memory from (i) perceptive information extracted in the road scene, and (ii) from permanent knowledge stored and activated in the long term memory, that are modelling in COSMODRIVE as Driving Schemas (Bellet et al, 1999). At the tactical level (Michon, 1985), this mental representation provides an egocentred and a goal-oriented understanding of the traffic situation, including anticipations of the future changes of the current driving situation. Moreover, car-driving is based on two different levels of activity control: an automatic and implicit mode versus an attentional and explicit mode (Bellet et al, 2009). This dichotomy is well established in scientific literature, for example, with the distinction put forward by Schneider and Schiffrin (1977) between controlled processes, which require cognitive resources and which can only be performed sequentially, and automatic processes, which can be performed in parallel without any attentional effort. In the same way, Rasmussen (1986) distinguishes different levels of activity control according to whether the behaviours implemented rely on (i) highly integrated sensorial-motor reflexes (Skill-based behaviours), (ii) well mastered decision rules for managing familiar situations (Rule-based behaviours), or (iii) more abstract and generic knowledge that is activated in new situations, for which the driver have not any prior experience (Knowledge-based behaviours).
2.1 Modelling the tactical cognition: the Driving Schemas Based on both the Piaget (1936) concept of operative scheme and the Minsky (1975) frames theory, the COSMODRIVE driving schema is a computational formalism defined at INRETS to model operative mental models of the driving activity “situated on the road”. They correspond to prototypical empirical situations, actions and events, learnt by the driver from practical experience. From a formal point of view (Fig. 3) a Driving Schema is composed of (i) a functional model of road Infrastructure, (ii) a Tactical Goal (e.g. turn left), (iii) a sequence of States and (iv) a set of Zones. Two types of zone are distinguished: Driving Zones (Zi) corresponding to the driving path of the vehicle as it progresses through the crossroads, and the Perceptive Exploration Zones (exi) in which the driver seeks information (e.g. potential events liable to occur). Each driving zone is linked to Actions to be implemented (e.g. braking or accelerating, in view to reach a given state at the end of the zone), the Conditions of performing these actions, and the perceptive exploration zones that permit checking these conditions (e.g. colour of traffic lights, presence of other road users). A State is characterized by the vehicle’s position and speed. The different sequences of the driving zones make up the Driving Paths that progress from the initial state to the final one (i.e. achievement of the tactical goal). ex6 ex4
high speed vehicle?
vehicle ? ex5
ex7 : pedestrian ? ex3 Z3b2 Z4
Z3b1 Z3a2
COgnitive Simulation MOdel of the DRIVEr
Z3a Z3a1
PERCEPTION MODULE
(COSMODRIVE)
Z2
Road Environment
ex1 : pedestrian ?
Initial State Local States Final State
COGNITION MODULE:
Driving Zones (Zi) Perceptive Exploration Zones (Exi) Driving Path alternatives
Situation Awareness & Decision-Making
(TACTICAL LEVEL)
ex2 : color of the traffic lights ?
Z1
Perception-Cognition-Action ACTION MODULE (OPERATIONAL LEVEL)
Loops of Control : - Automatic control mode (implicit) - Attentional control mode (explicit)
Fig. 2: COSMODRIVE regulation loops
By considering this theoretical background, the simplified version of the COSMODRIVE model to be implemented during the ISI-PADAS project will be composed of three main functional modules (i.e. the Perception, the Cognition, and the Action modules) and should be able to drive a virtual Vehicle in a virtual Environment through two synchronized “Perception-Cognition-Action” regulation loops (fig. 2): an attentional control mode (mainly focused on Rasmussen’s rule-based behaviours, and simulated in COSMODRIVE through the Driving Schemas theoretical approach), and an automatic control loop (corresponding to the skill-based behaviours simulated in COSMODRIVE through the Envelope Zones concept and the Pure-Pursuit Point method).
Fig. 3: The Driving Schemas Once activated in working memory and instantiated with the road environment, an active driving schema becomes the tactical mental representation of the driver, which will be continually updated as and when s/he progresses into the current road environment. Tactical representation correspond to the driver’s explicit awareness of the driving situation and provides a mental model of the road environment functionally structured, according to the tactical goal pursued by the driver in this particular context (e.g. turn left). 2.2 Operational cognitive level: the Envelope-Zones At the operational level, corresponding to the automatic control loop presented in fig. 2, the COSMODRIVE model regulation strategy is based on the “Envelope Zones”. From a theoretical point of view (Bellet et al., 2007), the concept of envelope zones recalls two classical theories in psychology: the notion of body image proposed by Schilder (1950), and
the theory of proxemics defined by Hall (1966), relating to the distance maintained in social interactions with other humans. Regarding car-driving activity, envelope zones also refer to the notion of safety margins (Gibson and Crooks, 1938), reused by several authors. At this last level, COSMODRIVE model approach (Fig.4) is more particularly based on Kontaratos work (1974), and distinguishes a safety zone, a threat zone, and a danger zone in which no other road user should enter (if this occurs, the driver automatically activates an emergency reaction).
cameras, GPS, laser scanners, IR transmitters, inertial navigation equipments, or odometers, RTMaps can then replay the recorded scenarios.
Fig. 3: The COSMODRIVE “Three Envelope-Zones” model
Fig. 4: Screenshot of the SiVIC- RTMaps platform
The envelope zones correspond to the portion of the path of driving schema to be occupied by the vehicle in the near future. Moreover, as an “hidden dimension” of the social cognition as described by Hall (1966), these proxemics zones are also mentally projected to other road users, and are then used to dynamically interact with other vehicles as well as to anticipate or manage the collision risks. 3. SiVIC: A VEHICLE-ENVIRONMENT PLATFORM SiVIC is a Vehicle-Environment (VE) platform developed by INRETS-LIVIC (Gruyer, et al., 2006) for virtual design of driving assistances. The main objective of this tool is the prototyping of virtual sensors for embedded systems, with respect to their physical capabilities and with the aim to provide real-time measurement of environmental behaviour changes including weather conditions, moving objects, infrastructures, or others dynamic events. Indeed, the design and the development of driving assistance devices generally requires to collect data through vehicles equipped with an embedded architecture of perception and control/command systems. However, it appears necessary to find solution of substitution in case of lack of real data or when scenarios are too dangerous or too difficult to be implemented in the real world. Moreover, virtual tools are needed to test and to evaluate embedded algorithms with very accurate references. The software architecture of SiVIC has been developed in that perspective. SiVIC models a virtual road environment including the vehicle, the infrastructure and the sensors. Moreover in order to achieve the test step of embedded software applications, an interconnection has been developed between SiVIC and RTMaps (Real Time, Multisensor, Advanced Prototyping Software). RTMaps is a modular environment used to embed the software applications in vehicles. The logic of the SiVIC software architecture is to reproduce, in the most faithful way, the reality of a situation as well as the behaviour of a vehicle and all its embedded sensors. Thus this platform gives the possibility to generate data that can be recorded by RTMaps (Steux, 2001) vehicle’s data acquisition system. This means managing a continuous flow of time-stamped and synchronized numerical data, from
Hence it is possible to share data and to avoid the investment related to real experiments at an early stage of the research and development programs. The coupling of SiVIC with RT Maps (Fig.5) brings RTMaps the ability to replace real-life data by simulated data. Moreover it also allows opening in RT Maps the perspective for prototyping on desktop the control/command algorithms since SiVIC takes advantage of a physical car model. The need for an equipped vehicle is no longer necessary for the first stages of the prototyping cycle. 3.1 Road Environment Modelling: the graphical 3D engine The graphical 3D engine of SIVIC has a decoupling between simulation and rendering process. This decoupling is made in order to get a simulation without the rendering stage or with a reduced number of rendering stages. In order to manage the temporal aspect, two time bases are available (CPU and virtual times). The modelling of 3D environment in SiVIC, is based on a tree of binary partitioning (BSP) in order to reduce the computing load of the processor. Moreover, functionalities have been added in order to handle specific sensor operating like Radar, reflection, weather constraint, movement of object without physical model. But for the realization and the simulation of a road scene, the 3D engine is not sufficient. It is also necessary to use a set of dynamic modules (i.e. plugsin) developed independently from the 3D engine. These plugins model and ensure the simulation of both the sensors and the vehicle. They have a dynamic mechanism of classes loading which allows the addition or destruction of actors during the simulation (sensors, vehicles or graphical objects) without compromising the working of the application. Along with this mechanism, a script language is integrated to dynamically manage and adjust the attributes and the actors of the scene. 3.2 Virtual sensors and vehicle modelling Indeed, SiVIC is not really an extension of the 3D engine, but an application using this graphical engine for the graphical rendering stage and including dynamically many external modules that simulate all the actors of a road situation. In order to have access to all the parameters of the sensors and the vehicles, the communication protocol uses the same rules
that the ones used in the graphical engine. Thus, the mechanism used for the communications protocol is modular and is distributed on all the SiVIC modules.
command scripts for a very complete and dynamic handling of the vehicle during the simulation stage.
Virtual sensors modelling (fig. 6): in order to be able to reproduce a coherent situation with the reality and to be able to generate all the data coming from the sensors embedded on the vehicles of the LIVIC, a set of sensors as well proprioceptive as exteroceptive are modelled and developed inside SiVIC. The main sensors modules currently available on SiVIC tool are video and fisheye cameras, odometers, laser scanners, inertial navigation system, and beacons.
4. COSMO-SIVIC: VIRTUAL SIMULATION PLATFORM FOR HUMAN CENTRED DESIGN OF DRIVING AIDS
Fig. 6. Examples of SiVIC use for virtual sensors simulation. Vehicle modelling (fig. 7): For providing realistic data for the embedded virtual sensors, it is necessary to reproduce the movement of the vehicle bodywork on the three axes (i.e. roll, pitch and head). These movements also must take into account the effects of the shock absorbers (pumping).
By interfacing COSMODRIVE and SiVIC, it becomes possible to have a virtual platform (i.e COSMO-SiVIC) able to generate dynamic simulations of a driver model interacting with a virtual road environment, through actions on a virtual vehicle. Nevertheless, this two pre-existing tools was not initially connected, and specific developments were required for interfacing them. First at all, a new version of COSMODRIVE has been defined in accordance with the ISiPADAS project objectives, in order to be implemented on the COSMO-SIVIC platform. This new COSMODRIVE version aims to simulate driving activity at four main different levels: (i) Perception of the road environment, (ii) Mental Representations elaboration (corresponding to the driver’s situational awareness), (iii) Decision-Making (based on the mental model of the current state of the world but also on anticipations generated through internal mental simulations) and (iv), Executives Functions, in order to dynamically drive a virtual car and to progress in the SIVIC road environment, through two synchronized and adaptive “PerceptionCognition-Action” regulation loops (Fig. 8). COSMODRIVE Virtual Driver
SIVIC Virtual Platform Virtual Eye (SIVIC Camera)
PERCEPTION MODULE
COGNITION MODULE Explicit Implicit Cognition Cognition (representations & decision)
Fig. 7: Vehicle modelling on SiVIC The vehicle model used in SiVIC is based on Glaser’s works (2004) and includes shock absorbers and non-linear tyre road forces. The contact modelling between the tyres and the road surface uses an effort formalization of either Bakker et al. (1989) or Dugoff et al. (1970). This model permits to simulate a coupling between longitudinal/lateral axes, the impact of the normal force variations and the moment of the car alignment. The modelling of the vehicle bodywork is made with an unbending suspended mass. The architecture used for coupling of the embedded sensors and a vehicle is a set of classes of abstract interfacing, allowing to easily install a great number of sensors onboard (in the vehicle). The module of vehicle management includes a model of vehicle, a solver, and a set of control law. Three different mode of management are possible on SiVIC: (i) Autonomous, (ii) Tracking, and (iii) Control/Command. The two last control modes allow to manage complex scenarios with many vehicles following their own instructions of trajectory. In the Tracking mode, the trajectory instructions and the integration of control laws allow to test and to evaluate lateral and longitudinal controls algorithms. The command input are the steering angle and the acceleration (torque on each wheel). Autonomous and Tracking modes also integrate a set of
3D Model of the External Road Environment
Perceptive Cycle
Virtual Car
(representations & decision)
“Perception-Cognition-Action” Regulation Loops (Attentional versus Automatic)
ACTION MODULE
Virtual Control/Command Functions
Fig. 8: Overview of COSMO-SiVIC platform These cognitive functions are currently under development, but the general principles for implementing them on COSMO-SIVIC are the following. 4.1 Cognitive functions simulation on COSMO-SIVIC Concerning Perception, this module is designed as a new type of SIVIC virtual sensor, which is derivated and adapted from the pre-existing camera model (cf. 3.2). Moreover, two complementary perceptive processes are in charge to simulate the human information processing while driving. The first one, perceptive integration, is a “data-driven” process (i.e. bottom-up integration) and allows cognitive integration of environmental information in the driver’s tactical mental representations. The second one, perceptive exploration (based on Neisser’s perceptive cycle, 1976), is a “knowledgedriven” process (i.e. top-down integration) in charge to continuously update the driver’s mental models and to actively explore the road scene, according to the expectations included in tactical representations (Perceptive Exploration Zones and Driving Zones of the driving schemas; cf. 2.1).
Concerning Mental Representation elaboration, this cognitive function is mainly based on the mental driving schemas instantiation. In order to applied the COSMODRIVE driving schema theory into the SiVIC virtual environment, it was needed to extend the pre-existing SiVIC environment models, by including remarkable points into the road infrastructure (Mayenobe et al., 2002). These Remarkable points are specific landmarks (e.g. the centre of an intersection, the position of traffic lights, the roads corners) used for matching of the qualitative geometry of the mental driving schemas (more particularly concerning the perceptive zones and the driving zones; cf. fig. 3), with the objective geometry of the physical road infrastructure, as virtually simulated in the SiVIC Platform. Therefore, the pre-existing SiVIC model of road infrastructures has been enhanced with a set of remarkable points in the new COSMO-SiVIC VE-platform. Two Decision-Making processes are implemented on COSMO-SIVIC, one for each regulation loops (i.e. attentional versus automatic) previously discussed in section 2. At the attentional level, corresponding to explicit decisions, this process is modelling through a set of State-Transition automats intimately linked with the driving path and conditions integrated in the tactical driving schemas. At the automatic level, corresponding to the automatic regulation loop, the implicit decision-making, is directly implemented at the operational level of vehicle control, to be described in the next section. Moreover, in order to support tactical decision-making based on cognitive anticipations (i.e. drivers’ abilities to project themselves into the future through mental simulations), that are implemented in COSMODRIVE as a process of mental deployment of the driving schemas (Bellet et al., 2009), the SiVIC 3D graphical engine is indeed dually used in COSMOSiVIC. As first instance is in charge to simulate the current road environment, and the other ones are used to dynamically simulate driver’s mental representations. In order to synchronise several instances of SiVIC (potentially implemented on different computers), a specific functionality has been developed in COSMO-SIVIC. This plug-in allows parallel execution of multiple instances of SiVIC that are synchronised through UDP/IP network connections. The synchronisation process may act at two levels: when loading a static environment into SiVIC, and when running simulations by synchronising the state of every dynamic item that evolves in the road environment. Technically, one instance of SiVIC acts as master and the other ones act as slaves. The former computes the simulation of the actual driving environment, and the latters are use to simulate the different driver’s internal mental models (i.e. the tactical representation of the current driving situation as well as the anticipated representations corresponding to the driver’s expectations) and only integrate the new status of the dynamic events, in accordance with the perceptive integration and perceptive exploration processes carried out by the Perception module. It is then possible to simulate human errors in terms of inadequate mental representations (non-integration of perceptive data or event false-updating due to distraction, for example) and, consequently, to generate and explain erroneous human decision-making.
4.1 Executive functions and vehicle control skills The COSMODRIVE vehicle-control abilities, that mainly corresponding to the automatic regulation loop, are based on two main mechanisms: the Envelope Zone regulation strategy (which as been discussed in section 2.2) and the Pure-Pursuit Point method (Mayenobe, 2004).
Fig. 9: the “Pure-Pursuit Point” method The Pure-Pursuit Point method (fig. 9) was initially introduced for modelling in a simplified way the lateral and the longitudinal controls of automatic cars along a trajectory (Amidi, 1990), and has been then adapted by Sukthankar (1997) for driver’s situational awareness modelling. Mathematically, the pure-pursuit point is defined as the intersection of the desired vehicle path and a circle of radius centred at the vehicle’s rear axle midpoint (assuming front wheel steer). Intuitively, this point describes the steering curvature that would bring the vehicle to the desired lateral offset after travelling a distance of approximately l. Thus the position of the pure-pursuit point maps directly onto a recommended steering curvature: k = -2x/l, where k is the curvature (reciprocal of steering radius), x is the relative lateral offset to the pure-pursuit point in vehicle coordinates, and l is a parameter known as the look-ahead distance. According to this definition, the operational control of the car by COSMODRIVE on COSMO-SIVIC can be seen as an automatic regulation loop that permanently maintaining of the Pursuit Point in the tactical driving path, to a given speed assigned with each segment of the current tactical driving schema, as instantiated in the mental representation.
Fig. 5: Visualisation of COMODRIVE Pursuit Point and Envelope Zones regulation strategies on the COSMO-SIVIC platform .
These two vehicle-control abilities have been implemented on COSMO-SIVIC platform as a new mode of the preexisting SIVIC models of vehicles control (see section 3.2). Indeed, a new class of “COSMO-CAR” objects based on the “SivicCar” pre-existing class has been thus defined, in order to provide new specific COSMO-SIVIC car-models able to integrate both the pure-pursuit point method for monitoring their lateral and their longitudinal controls, and the envelope zones strategy, for managing their interactions with the other road users. The Figure 10 illustrates such a regulation strategy in the frame of a car-following task: the pursuit point
determines the cap to be followed, and then the envelope zones are used for regulating the distance with the lead car. 5. CONCLUSIONS AND PERSPECTIVES Vehicle automation is a very complex issue that needs considerable effort from both engineering (for technological developments) and ergonomics (in order to integrate endusers needs during the design process). Providing simulation tools able to simulate human needs and performances since the earlier stages of the technological design process (i.e. before the development of expensive prototypes) is thus a crucial challenge in the near future. Indeed, such virtual Driver-Vehicle-Environment platforms are necessary for promoting Human centred design approach, and thus increasing the future systems efficiency, effectiveness and acceptance by end-users. The COSMO-SIVIC platform presented in this paper constitutes a first step towards virtual design of driving assistances. In its current status, it allows to simulate several drivers’ cognitive abilities and behavioural performances - via a virtual car - in order to progress in a virtual road environment, according 2 adaptive “PerceptionCognition-Action” regulation loops. The next step is to introduce virtual models of driving assistances on COSMOSIVIC, with the aim to identify critical driving scenarios for which vehicle automation could provide a relevant solution. In a more long term perspective, it is also expected to compare simulation results of the driving performances with and without driving assistance, in order to appreciate the respective benefits versus the potential risks on road safety of different versions of driving assistances. ACKNOWLEDGEMENT: The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP7/2007-2013) under grant agreement n°218552 Project ISi-PADAS. REFERENCES Amidi O (1990), Integrated mobile robot control, Technical Report CMU-RI-TR-90-17, Carnegie Mellon University Robotics Institute. Bainbridge L. (1987). Ironies of automation. In Rasmussen J., Duncan J. & Leplat J. (Eds.), New Technology and human errors, 271-286, Wiley, New York. Bakker E, Pacejka H.B., Lidner L. (1989), A new tire model with an application in vehicle dynamics studies, SEA paper n° 890087. Bellet T. (1998). Modélisation et simulation cognitive de l’opérateur humain : une application à la conduite automobile, Thèse de Doctorat, Université Paris V. Bellet, T., Bailly-Asuni, B., Mayenobe, P., Banet, A. (2009). A theoretical and methodological framework for studying and modelling drivers’ mental representations, Safety Science, 47, pp. 1205–1221. Bellet, T., Bailly, B., Mayenobe, P., Georgeon, O. (2007). Cognitive modelling and computational simulation of drivers mental activities. In: P.C. Cacciabue (Ed.), Modelling Driver Behaviour in Automotive Environment:
Critical Issues in Driver Interactions with Intelligent Transport Systems, 315-343, Springer Verlag. Bellet, T., Tattegrain-Veste, H. (1999). A framework for Representing Driving Knowledge. International Journal of Cognitive Ergonomics, 3 (1), 37-49. Dugoff J., Flanches P., Segal L. (1970). An analysis of tire traction properties and their influence on vehicle dynamic performance, SEA paper n° 700377. Endsley M.R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37 (1), 32-64. Glaser S. (2004). Modélisation et analyse d’un véhicule en trajectoires limites. Application au développement de systèmes d’aide à la conduite , Thèse de doctorat de l’Université d’Evry. Gibson J.J., Crooks L.E. (1938). A theoretical field-analysis of automobile driving. American Journal of Psychology, 51, pp. 453-471. Gruyer D., Royère C., du Lac N., Michel G., Blosseville J.M. (2006). SiVIC and RT-MAPS Interconnected platforms for the conception and the evaluation of driving assistance systems. Proceedings of the ITS World Congress, London, UK, october. Hall E.T. (1966). The hidden dimension. Doubleday, New York. Mayenobe P. (2004). Perception de l’environnement pour une gestion contextualisée de la coopération HommeMachine. PhD thesis, University Blaise Pascal de Clermont-Ferrand. Mayenobe P, Trassoudaine L, Bellet T., Tattegrain-Veste H. (2002). Cognitive Simulation of the Driver and Cooperative Driving Assistances. In Proceedings of the IEEE Intelligent Vehicles Symposium: IV-2002, Versailles, June 17-21, 265-271. Michon J.A. (1985). A critical view of driver behavior models : what do we know, what should we do ?. In: Evans, L., Schwing, R.C. (Eds), Human behavior and traffic safety, pp. 485-520, Plenum Press, New York. Minsky M. (1975). A Framework for Representing Knowledge. In Winston, P.H. (Ed.), The Psychology of Computer Vision, 211-277, Mc Graw-Hill, New York. Neisser U. (1976). Cognition and reality: principles and implications of cognitive psychology. W. H. Freeman, San Francisco. Piaget J. (1936). La Naissance de l'intelligence chez l'enfant, Delachaux & Niestlé, Neuchâtel. Rasmussen J. (1986). Information processing and humanmachine interaction: an approach to cognitive engineering, Amsterdam, North Holland. Scheinder W., Shiffrin R.M. (1977). Controlled and automatic human information processing I: Detection, search and attention. Psychological Review, 84, 1-88. Steux B. (2001). RTMaps: un environnement logiciel dédié à la conception d’applications embarquées temps-réel. Utilisation pour la détection automatique de véhicules par fusion radar/vision , Thèse de Doctorat de l’Ecole des Mines de Paris. Sukthankar R. (1997). Situation Awareness for Tactical Driving, Phd thesis, Carnegie Mellon University, Pittsburgh, PA, United States of America.
*Institut National de Recherche sur les Transports et leur Sécurité (LESCOT: Laboratoire Ergonomie et Sciences Cognitives), 69675 Bron - France (Tel: 33 (0)4 72 14 24 57; e-mail: bellet@ inrets.fr) ** Institut National de Recherche sur les Transports et leur Sécurité - Laboratoire Central des Ponts et Chaussées (LIVIC: Laboratoire Interaction Véhicule-Infrastructure-Conducteur), 78000 Versailles-Satory (e-mail: [email protected]) Abstract: This paper presents the first step of research work implemented by INRETS in the frame of the ISi-PADAS European project, in order to develop a simulation platform able to support a Human Centred Design (HCD) method for virtual design of driving assistances. This HCD tool (called COSMO-SiVIC) integrates a cognitive simulation model of the Driver (called COSMODRIVE) on a virtual VehicleEnvironment platform (SiVIC). From this future tool, it is expected to compare since the earlier stages of the technological design, virtual simulation of driving performances with and without driving assistance, and thus to appreciate the potential benefits, interests and risks of vehicle automation on road safety. Keywords: Driver modelling, Cognitive simulation, Virtual Human Centred Design, Vehicle automation. 1. INTRODUCTION: CONTEXTE AND OBJECTIVES Technological advances in car industry during the last decade have now made possible to design automated systems able to takeover the vehicle in normal driving conditions (e.g. adaptive cruise control) as well as in critical situations (to avoid collisions or lane departure, for example). Such a “revolution" will radically modify the driving activity in the near future in term of vehicle control sharing between the human driver and the automatisms. However, automation past efforts in other areas, like nuclear plants or aviation, have demonstrated potential risks on safety (e.g. the “ironies of automation” of Bainbridge, 1987): automation may be source of specific human errors and may cause critical conflicts between human and machine. To maintain human operator “in the loop” of control is also essential in case of automation malfunction, or when the driving situation comes out of the validity limits of the assistance. Consequently, the decision and the way to use automation in order to assist, or to replace the human driver must not only take into account the technological capabilities themselves, but also the human needs, their own abilities, and their acceptance regarding technological assistances. A Human-Centred design approach of these devices is particularly important in car driving context, by considering the heterogeneity of the drivers, the potential risk of the driving task, the variability of the driving situations and, lastly, the responsibility issue in case of accident. However, integrating end-users needs is not always easy, especially when one wishes to develop innovative devices often costly to develop. Ergonomics evaluations with real drivers indeed required to develop mock-ups and operational prototypes which are generally expensive, and may cost a lot of time. In order to better promote the human drivers’ needs, characteristics and abilities since the early stages of technological design, a new generation of virtual tools is necessary. Such virtual tools should jointly include models of (i) the Driver, (ii) the Vehicle, and (iii) the road
Environment (i.e. DVE platform), in order to virtually assess the driving assistance effects through simulation. That is the global objective of the ISi-PADAS European project (Integrated human modelling and Simulation to support human error risk analysis of Partially Autonomous Driver Assistance Systems). The research presented in this paper takes place in this Human Centred Design approach, aiming at setting up a virtual simulation platform to design and evaluate in-vehicle systems interest and potential impact on road safety. In this objective, it is proposed as a first step to implement a cognitive simulation model of the Driver (called COSMODRIVE) on a Vehicle-Environment platform (called SiVIC), in order to provide a DVE simulation platform, COSMO-SiVIC, liable to .support in the future the virtual design of vehicle automation technologies. 2. COSMODRIVE: A COGNITIVE SIMULATION MODEL OF THE DRIVER COSMODRIVE is a COgnitive Simulation MOdel of the DRIVEr developed at INRETS-LESCOT in order to simulate all the driver’s mental activities carried out while driving, from perceptive functions to behavioural performances (Bellet, 1998; Bellet et al., 2007). SITUATION AWARENESS
PERCEPTION COGNITION
DECISION MAKING MENTAL REPRESENTATION
Perceptive Cycle ACTION PLANNING
COGNITION
PERCEPTION
Regulation Loop ACTION
ACTION IMPLEMENTATION (Executive Functions)
Fig. 1: Driving activity modelling in COSMODRIVE
Synthetically, the heart of COSMODRIVE theoretical model are the drivers’ mental representations of the driving environment, corresponding to the driver’s Situation Awareness, according to Endsley (1995) definition of this concept: the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. This mental models are built in working memory from (i) perceptive information extracted in the road scene, and (ii) from permanent knowledge stored and activated in the long term memory, that are modelling in COSMODRIVE as Driving Schemas (Bellet et al, 1999). At the tactical level (Michon, 1985), this mental representation provides an egocentred and a goal-oriented understanding of the traffic situation, including anticipations of the future changes of the current driving situation. Moreover, car-driving is based on two different levels of activity control: an automatic and implicit mode versus an attentional and explicit mode (Bellet et al, 2009). This dichotomy is well established in scientific literature, for example, with the distinction put forward by Schneider and Schiffrin (1977) between controlled processes, which require cognitive resources and which can only be performed sequentially, and automatic processes, which can be performed in parallel without any attentional effort. In the same way, Rasmussen (1986) distinguishes different levels of activity control according to whether the behaviours implemented rely on (i) highly integrated sensorial-motor reflexes (Skill-based behaviours), (ii) well mastered decision rules for managing familiar situations (Rule-based behaviours), or (iii) more abstract and generic knowledge that is activated in new situations, for which the driver have not any prior experience (Knowledge-based behaviours).
2.1 Modelling the tactical cognition: the Driving Schemas Based on both the Piaget (1936) concept of operative scheme and the Minsky (1975) frames theory, the COSMODRIVE driving schema is a computational formalism defined at INRETS to model operative mental models of the driving activity “situated on the road”. They correspond to prototypical empirical situations, actions and events, learnt by the driver from practical experience. From a formal point of view (Fig. 3) a Driving Schema is composed of (i) a functional model of road Infrastructure, (ii) a Tactical Goal (e.g. turn left), (iii) a sequence of States and (iv) a set of Zones. Two types of zone are distinguished: Driving Zones (Zi) corresponding to the driving path of the vehicle as it progresses through the crossroads, and the Perceptive Exploration Zones (exi) in which the driver seeks information (e.g. potential events liable to occur). Each driving zone is linked to Actions to be implemented (e.g. braking or accelerating, in view to reach a given state at the end of the zone), the Conditions of performing these actions, and the perceptive exploration zones that permit checking these conditions (e.g. colour of traffic lights, presence of other road users). A State is characterized by the vehicle’s position and speed. The different sequences of the driving zones make up the Driving Paths that progress from the initial state to the final one (i.e. achievement of the tactical goal). ex6 ex4
high speed vehicle?
vehicle ? ex5
ex7 : pedestrian ? ex3 Z3b2 Z4
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COgnitive Simulation MOdel of the DRIVEr
Z3a Z3a1
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Driving Zones (Zi) Perceptive Exploration Zones (Exi) Driving Path alternatives
Situation Awareness & Decision-Making
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ex2 : color of the traffic lights ?
Z1
Perception-Cognition-Action ACTION MODULE (OPERATIONAL LEVEL)
Loops of Control : - Automatic control mode (implicit) - Attentional control mode (explicit)
Fig. 2: COSMODRIVE regulation loops
By considering this theoretical background, the simplified version of the COSMODRIVE model to be implemented during the ISI-PADAS project will be composed of three main functional modules (i.e. the Perception, the Cognition, and the Action modules) and should be able to drive a virtual Vehicle in a virtual Environment through two synchronized “Perception-Cognition-Action” regulation loops (fig. 2): an attentional control mode (mainly focused on Rasmussen’s rule-based behaviours, and simulated in COSMODRIVE through the Driving Schemas theoretical approach), and an automatic control loop (corresponding to the skill-based behaviours simulated in COSMODRIVE through the Envelope Zones concept and the Pure-Pursuit Point method).
Fig. 3: The Driving Schemas Once activated in working memory and instantiated with the road environment, an active driving schema becomes the tactical mental representation of the driver, which will be continually updated as and when s/he progresses into the current road environment. Tactical representation correspond to the driver’s explicit awareness of the driving situation and provides a mental model of the road environment functionally structured, according to the tactical goal pursued by the driver in this particular context (e.g. turn left). 2.2 Operational cognitive level: the Envelope-Zones At the operational level, corresponding to the automatic control loop presented in fig. 2, the COSMODRIVE model regulation strategy is based on the “Envelope Zones”. From a theoretical point of view (Bellet et al., 2007), the concept of envelope zones recalls two classical theories in psychology: the notion of body image proposed by Schilder (1950), and
the theory of proxemics defined by Hall (1966), relating to the distance maintained in social interactions with other humans. Regarding car-driving activity, envelope zones also refer to the notion of safety margins (Gibson and Crooks, 1938), reused by several authors. At this last level, COSMODRIVE model approach (Fig.4) is more particularly based on Kontaratos work (1974), and distinguishes a safety zone, a threat zone, and a danger zone in which no other road user should enter (if this occurs, the driver automatically activates an emergency reaction).
cameras, GPS, laser scanners, IR transmitters, inertial navigation equipments, or odometers, RTMaps can then replay the recorded scenarios.
Fig. 3: The COSMODRIVE “Three Envelope-Zones” model
Fig. 4: Screenshot of the SiVIC- RTMaps platform
The envelope zones correspond to the portion of the path of driving schema to be occupied by the vehicle in the near future. Moreover, as an “hidden dimension” of the social cognition as described by Hall (1966), these proxemics zones are also mentally projected to other road users, and are then used to dynamically interact with other vehicles as well as to anticipate or manage the collision risks. 3. SiVIC: A VEHICLE-ENVIRONMENT PLATFORM SiVIC is a Vehicle-Environment (VE) platform developed by INRETS-LIVIC (Gruyer, et al., 2006) for virtual design of driving assistances. The main objective of this tool is the prototyping of virtual sensors for embedded systems, with respect to their physical capabilities and with the aim to provide real-time measurement of environmental behaviour changes including weather conditions, moving objects, infrastructures, or others dynamic events. Indeed, the design and the development of driving assistance devices generally requires to collect data through vehicles equipped with an embedded architecture of perception and control/command systems. However, it appears necessary to find solution of substitution in case of lack of real data or when scenarios are too dangerous or too difficult to be implemented in the real world. Moreover, virtual tools are needed to test and to evaluate embedded algorithms with very accurate references. The software architecture of SiVIC has been developed in that perspective. SiVIC models a virtual road environment including the vehicle, the infrastructure and the sensors. Moreover in order to achieve the test step of embedded software applications, an interconnection has been developed between SiVIC and RTMaps (Real Time, Multisensor, Advanced Prototyping Software). RTMaps is a modular environment used to embed the software applications in vehicles. The logic of the SiVIC software architecture is to reproduce, in the most faithful way, the reality of a situation as well as the behaviour of a vehicle and all its embedded sensors. Thus this platform gives the possibility to generate data that can be recorded by RTMaps (Steux, 2001) vehicle’s data acquisition system. This means managing a continuous flow of time-stamped and synchronized numerical data, from
Hence it is possible to share data and to avoid the investment related to real experiments at an early stage of the research and development programs. The coupling of SiVIC with RT Maps (Fig.5) brings RTMaps the ability to replace real-life data by simulated data. Moreover it also allows opening in RT Maps the perspective for prototyping on desktop the control/command algorithms since SiVIC takes advantage of a physical car model. The need for an equipped vehicle is no longer necessary for the first stages of the prototyping cycle. 3.1 Road Environment Modelling: the graphical 3D engine The graphical 3D engine of SIVIC has a decoupling between simulation and rendering process. This decoupling is made in order to get a simulation without the rendering stage or with a reduced number of rendering stages. In order to manage the temporal aspect, two time bases are available (CPU and virtual times). The modelling of 3D environment in SiVIC, is based on a tree of binary partitioning (BSP) in order to reduce the computing load of the processor. Moreover, functionalities have been added in order to handle specific sensor operating like Radar, reflection, weather constraint, movement of object without physical model. But for the realization and the simulation of a road scene, the 3D engine is not sufficient. It is also necessary to use a set of dynamic modules (i.e. plugsin) developed independently from the 3D engine. These plugins model and ensure the simulation of both the sensors and the vehicle. They have a dynamic mechanism of classes loading which allows the addition or destruction of actors during the simulation (sensors, vehicles or graphical objects) without compromising the working of the application. Along with this mechanism, a script language is integrated to dynamically manage and adjust the attributes and the actors of the scene. 3.2 Virtual sensors and vehicle modelling Indeed, SiVIC is not really an extension of the 3D engine, but an application using this graphical engine for the graphical rendering stage and including dynamically many external modules that simulate all the actors of a road situation. In order to have access to all the parameters of the sensors and the vehicles, the communication protocol uses the same rules
that the ones used in the graphical engine. Thus, the mechanism used for the communications protocol is modular and is distributed on all the SiVIC modules.
command scripts for a very complete and dynamic handling of the vehicle during the simulation stage.
Virtual sensors modelling (fig. 6): in order to be able to reproduce a coherent situation with the reality and to be able to generate all the data coming from the sensors embedded on the vehicles of the LIVIC, a set of sensors as well proprioceptive as exteroceptive are modelled and developed inside SiVIC. The main sensors modules currently available on SiVIC tool are video and fisheye cameras, odometers, laser scanners, inertial navigation system, and beacons.
4. COSMO-SIVIC: VIRTUAL SIMULATION PLATFORM FOR HUMAN CENTRED DESIGN OF DRIVING AIDS
Fig. 6. Examples of SiVIC use for virtual sensors simulation. Vehicle modelling (fig. 7): For providing realistic data for the embedded virtual sensors, it is necessary to reproduce the movement of the vehicle bodywork on the three axes (i.e. roll, pitch and head). These movements also must take into account the effects of the shock absorbers (pumping).
By interfacing COSMODRIVE and SiVIC, it becomes possible to have a virtual platform (i.e COSMO-SiVIC) able to generate dynamic simulations of a driver model interacting with a virtual road environment, through actions on a virtual vehicle. Nevertheless, this two pre-existing tools was not initially connected, and specific developments were required for interfacing them. First at all, a new version of COSMODRIVE has been defined in accordance with the ISiPADAS project objectives, in order to be implemented on the COSMO-SIVIC platform. This new COSMODRIVE version aims to simulate driving activity at four main different levels: (i) Perception of the road environment, (ii) Mental Representations elaboration (corresponding to the driver’s situational awareness), (iii) Decision-Making (based on the mental model of the current state of the world but also on anticipations generated through internal mental simulations) and (iv), Executives Functions, in order to dynamically drive a virtual car and to progress in the SIVIC road environment, through two synchronized and adaptive “PerceptionCognition-Action” regulation loops (Fig. 8). COSMODRIVE Virtual Driver
SIVIC Virtual Platform Virtual Eye (SIVIC Camera)
PERCEPTION MODULE
COGNITION MODULE Explicit Implicit Cognition Cognition (representations & decision)
Fig. 7: Vehicle modelling on SiVIC The vehicle model used in SiVIC is based on Glaser’s works (2004) and includes shock absorbers and non-linear tyre road forces. The contact modelling between the tyres and the road surface uses an effort formalization of either Bakker et al. (1989) or Dugoff et al. (1970). This model permits to simulate a coupling between longitudinal/lateral axes, the impact of the normal force variations and the moment of the car alignment. The modelling of the vehicle bodywork is made with an unbending suspended mass. The architecture used for coupling of the embedded sensors and a vehicle is a set of classes of abstract interfacing, allowing to easily install a great number of sensors onboard (in the vehicle). The module of vehicle management includes a model of vehicle, a solver, and a set of control law. Three different mode of management are possible on SiVIC: (i) Autonomous, (ii) Tracking, and (iii) Control/Command. The two last control modes allow to manage complex scenarios with many vehicles following their own instructions of trajectory. In the Tracking mode, the trajectory instructions and the integration of control laws allow to test and to evaluate lateral and longitudinal controls algorithms. The command input are the steering angle and the acceleration (torque on each wheel). Autonomous and Tracking modes also integrate a set of
3D Model of the External Road Environment
Perceptive Cycle
Virtual Car
(representations & decision)
“Perception-Cognition-Action” Regulation Loops (Attentional versus Automatic)
ACTION MODULE
Virtual Control/Command Functions
Fig. 8: Overview of COSMO-SiVIC platform These cognitive functions are currently under development, but the general principles for implementing them on COSMO-SIVIC are the following. 4.1 Cognitive functions simulation on COSMO-SIVIC Concerning Perception, this module is designed as a new type of SIVIC virtual sensor, which is derivated and adapted from the pre-existing camera model (cf. 3.2). Moreover, two complementary perceptive processes are in charge to simulate the human information processing while driving. The first one, perceptive integration, is a “data-driven” process (i.e. bottom-up integration) and allows cognitive integration of environmental information in the driver’s tactical mental representations. The second one, perceptive exploration (based on Neisser’s perceptive cycle, 1976), is a “knowledgedriven” process (i.e. top-down integration) in charge to continuously update the driver’s mental models and to actively explore the road scene, according to the expectations included in tactical representations (Perceptive Exploration Zones and Driving Zones of the driving schemas; cf. 2.1).
Concerning Mental Representation elaboration, this cognitive function is mainly based on the mental driving schemas instantiation. In order to applied the COSMODRIVE driving schema theory into the SiVIC virtual environment, it was needed to extend the pre-existing SiVIC environment models, by including remarkable points into the road infrastructure (Mayenobe et al., 2002). These Remarkable points are specific landmarks (e.g. the centre of an intersection, the position of traffic lights, the roads corners) used for matching of the qualitative geometry of the mental driving schemas (more particularly concerning the perceptive zones and the driving zones; cf. fig. 3), with the objective geometry of the physical road infrastructure, as virtually simulated in the SiVIC Platform. Therefore, the pre-existing SiVIC model of road infrastructures has been enhanced with a set of remarkable points in the new COSMO-SiVIC VE-platform. Two Decision-Making processes are implemented on COSMO-SIVIC, one for each regulation loops (i.e. attentional versus automatic) previously discussed in section 2. At the attentional level, corresponding to explicit decisions, this process is modelling through a set of State-Transition automats intimately linked with the driving path and conditions integrated in the tactical driving schemas. At the automatic level, corresponding to the automatic regulation loop, the implicit decision-making, is directly implemented at the operational level of vehicle control, to be described in the next section. Moreover, in order to support tactical decision-making based on cognitive anticipations (i.e. drivers’ abilities to project themselves into the future through mental simulations), that are implemented in COSMODRIVE as a process of mental deployment of the driving schemas (Bellet et al., 2009), the SiVIC 3D graphical engine is indeed dually used in COSMOSiVIC. As first instance is in charge to simulate the current road environment, and the other ones are used to dynamically simulate driver’s mental representations. In order to synchronise several instances of SiVIC (potentially implemented on different computers), a specific functionality has been developed in COSMO-SIVIC. This plug-in allows parallel execution of multiple instances of SiVIC that are synchronised through UDP/IP network connections. The synchronisation process may act at two levels: when loading a static environment into SiVIC, and when running simulations by synchronising the state of every dynamic item that evolves in the road environment. Technically, one instance of SiVIC acts as master and the other ones act as slaves. The former computes the simulation of the actual driving environment, and the latters are use to simulate the different driver’s internal mental models (i.e. the tactical representation of the current driving situation as well as the anticipated representations corresponding to the driver’s expectations) and only integrate the new status of the dynamic events, in accordance with the perceptive integration and perceptive exploration processes carried out by the Perception module. It is then possible to simulate human errors in terms of inadequate mental representations (non-integration of perceptive data or event false-updating due to distraction, for example) and, consequently, to generate and explain erroneous human decision-making.
4.1 Executive functions and vehicle control skills The COSMODRIVE vehicle-control abilities, that mainly corresponding to the automatic regulation loop, are based on two main mechanisms: the Envelope Zone regulation strategy (which as been discussed in section 2.2) and the Pure-Pursuit Point method (Mayenobe, 2004).
Fig. 9: the “Pure-Pursuit Point” method The Pure-Pursuit Point method (fig. 9) was initially introduced for modelling in a simplified way the lateral and the longitudinal controls of automatic cars along a trajectory (Amidi, 1990), and has been then adapted by Sukthankar (1997) for driver’s situational awareness modelling. Mathematically, the pure-pursuit point is defined as the intersection of the desired vehicle path and a circle of radius centred at the vehicle’s rear axle midpoint (assuming front wheel steer). Intuitively, this point describes the steering curvature that would bring the vehicle to the desired lateral offset after travelling a distance of approximately l. Thus the position of the pure-pursuit point maps directly onto a recommended steering curvature: k = -2x/l, where k is the curvature (reciprocal of steering radius), x is the relative lateral offset to the pure-pursuit point in vehicle coordinates, and l is a parameter known as the look-ahead distance. According to this definition, the operational control of the car by COSMODRIVE on COSMO-SIVIC can be seen as an automatic regulation loop that permanently maintaining of the Pursuit Point in the tactical driving path, to a given speed assigned with each segment of the current tactical driving schema, as instantiated in the mental representation.
Fig. 5: Visualisation of COMODRIVE Pursuit Point and Envelope Zones regulation strategies on the COSMO-SIVIC platform .
These two vehicle-control abilities have been implemented on COSMO-SIVIC platform as a new mode of the preexisting SIVIC models of vehicles control (see section 3.2). Indeed, a new class of “COSMO-CAR” objects based on the “SivicCar” pre-existing class has been thus defined, in order to provide new specific COSMO-SIVIC car-models able to integrate both the pure-pursuit point method for monitoring their lateral and their longitudinal controls, and the envelope zones strategy, for managing their interactions with the other road users. The Figure 10 illustrates such a regulation strategy in the frame of a car-following task: the pursuit point
determines the cap to be followed, and then the envelope zones are used for regulating the distance with the lead car. 5. CONCLUSIONS AND PERSPECTIVES Vehicle automation is a very complex issue that needs considerable effort from both engineering (for technological developments) and ergonomics (in order to integrate endusers needs during the design process). Providing simulation tools able to simulate human needs and performances since the earlier stages of the technological design process (i.e. before the development of expensive prototypes) is thus a crucial challenge in the near future. Indeed, such virtual Driver-Vehicle-Environment platforms are necessary for promoting Human centred design approach, and thus increasing the future systems efficiency, effectiveness and acceptance by end-users. The COSMO-SIVIC platform presented in this paper constitutes a first step towards virtual design of driving assistances. In its current status, it allows to simulate several drivers’ cognitive abilities and behavioural performances - via a virtual car - in order to progress in a virtual road environment, according 2 adaptive “PerceptionCognition-Action” regulation loops. The next step is to introduce virtual models of driving assistances on COSMOSIVIC, with the aim to identify critical driving scenarios for which vehicle automation could provide a relevant solution. In a more long term perspective, it is also expected to compare simulation results of the driving performances with and without driving assistance, in order to appreciate the respective benefits versus the potential risks on road safety of different versions of driving assistances. ACKNOWLEDGEMENT: The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP7/2007-2013) under grant agreement n°218552 Project ISi-PADAS. REFERENCES Amidi O (1990), Integrated mobile robot control, Technical Report CMU-RI-TR-90-17, Carnegie Mellon University Robotics Institute. Bainbridge L. (1987). Ironies of automation. In Rasmussen J., Duncan J. & Leplat J. (Eds.), New Technology and human errors, 271-286, Wiley, New York. Bakker E, Pacejka H.B., Lidner L. (1989), A new tire model with an application in vehicle dynamics studies, SEA paper n° 890087. Bellet T. (1998). Modélisation et simulation cognitive de l’opérateur humain : une application à la conduite automobile, Thèse de Doctorat, Université Paris V. Bellet, T., Bailly-Asuni, B., Mayenobe, P., Banet, A. (2009). A theoretical and methodological framework for studying and modelling drivers’ mental representations, Safety Science, 47, pp. 1205–1221. Bellet, T., Bailly, B., Mayenobe, P., Georgeon, O. (2007). Cognitive modelling and computational simulation of drivers mental activities. In: P.C. Cacciabue (Ed.), Modelling Driver Behaviour in Automotive Environment:
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