PAAMS 2009

1 Renault, Technical Center for Simulation2 LIFL, University of Lille March 26, 2009

Generating Various and Consistent Behaviors

in SimulationsBenoît Lacroix 1,2, Philippe Mathieu 2 and Andras Kemeny 1

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 2

Context and motivation

Renault / LIFL UMR CNRS collaboration

Context: traffic simulation in driving simulators

Evaluation of ergonomics, embedded systems, design…

Needs

Various and consistent behaviors for autonomous vehicles (cautious, aggressive…)

Usable by scenario designers

Idea

Driving psychologists classify drivers depending on their behavior (Saad, 1992)

Drivers use set of norms (based on Highway Code, informal rules…)

But they do not strictly follow these norms

Generic approach to address the issue

Behaviors description using norms

Generation engine managing the determinism

Monitoring

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 3

Normative description of behaviors

Normative systems (Noriega, 1997; Esteva et al., 2001; Vazquez-Salceda et al., 2005)

Organizational control in multi-agent based simulations

Improve agents coordination, communication…

In our case

Institution: parameters and associated definition domains

Norms: subsets of these parameters and domains

Behaviors: instantiations of these norms

For instance, in traffic

Parameters: maximal speed, safety time…

Institution: bounds of these parameters (max speed in [0,300] km/h)

Norms: cautious, aggressive drivers (max speed in [140,160] km/h)

Behavior: a cautious, an aggressive (max speed = 156 km/h)

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 4

Generation engine

Variety

Randomly select parameters

from a norm

Behavioral variety within a

norm

Allow violations: one or more

parameters outside the limits

Consistency

Guaranteed when generation within norms limits

Mechanism to reject aberrant behaviors (quantification)

Reaction to violations at runtime

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 5

Monitoring

Emergence of new norms

Feedback to the users

Improve design and calibration

Calibration with real data

Learning norms from real data sets

Unsupervised learning

Kohonen Neural Networks

Description of the data space

Linear component analysis

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 6

Application

Application

Driving simulation software SCANeR™ II

Ergonomics, embedded systems, design, headlights…

Description

Agents’ decision model: perception – decision (finite state automata) –action (vehicle dynamic model)

Institution parameters = existing vehicles parameters of traffic model

Traffic managed by the existing model

Uses

Introduction of driving styles

Generation of the “ambient” traffic

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 7

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 8

Experimental results

Highway database 11 km, 3000 veh/h

Normal, aggressive and

cautious drivers

Speed distributions More norms increase variety

Increased dynamicity

Lane repartition Aggressive on left lane

Cautious on right lane

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 9

Conclusion

Easily create various behaviors

Manage the generation process

Guaranty the consistency of the behaviors

Allow violations if wished

Wide application range

Non-intrusive

Perspectives

Norms calibration with real data

Benoit Lacroix

Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 10

Thank you for your attention

Contact: benoit.lacroix@gmail.com