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Generating Various and Consistent Behaviors
      in Simulations
      Benoît Lacroix 1,2, Philippe Mathieu 2 and Andras Kemeny 1




1   Renault, Technical Center for Simulation
2   LIFL, University of Lille                  March 26, 2009   PAAMS 2009
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             2
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                 3
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   4
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   5
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    6
Benoit Lacroix
Renault, Technical Center for Simulation   March 26, 2009   PAAMS 2009   7
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   8
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   9
Thank you for your attention




                                           Contact: benoit.lacroix@gmail.com




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

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Generating Various and Consistent Behaviors in Simulations

  • 1. Generating Various and Consistent Behaviors in Simulations Benoît Lacroix 1,2, Philippe Mathieu 2 and Andras Kemeny 1 1 Renault, Technical Center for Simulation 2 LIFL, University of Lille 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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 6
  • 7. Benoit Lacroix Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 7
  • 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 8
  • 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 9
  • 10. Thank you for your attention Contact: benoit.lacroix@gmail.com Benoit Lacroix Renault, Technical Center for Simulation March 26, 2009 PAAMS 2009 10