Using ego-centered affordances in multi-agent traffic simulation

To improve the validity of traffic simulations in urban and suburban areas, we propose to consider the driving context and the driver behavior in terms of space occupation. We endow agent driver with an ego-centered representation of the environment. This representation permits the agent to take a decision in terms of space occupation. Our agent driver model is based on the concept of affordances - the ways in which an agent can interact with its environment. First, we use the concept of affordances to identify the possible actions, in terms of space occupation, afforded by the environment. Second, we use an ego-centered representation of the situation around the agent, composed by the identified affordances. The proposed driver model was implemented with ArchiSim and the experiments show that this model makes traffic more fluid.

[1]  Martin Raubal,et al.  Ontology and epistemology for agent-based wayfinding simulation , 2001, Int. J. Geogr. Inf. Sci..

[2]  Alexander Stoytchev,et al.  Behavior-Grounded Representation of Tool Affordances , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[3]  Petros Faloutsos,et al.  Egocentric affordance fields in pedestrian steering , 2009, I3D '09.

[4]  Ana L. C. Bazzan,et al.  Agents in Traffic Modelling - From Reactive to Social Behaviour , 1999, KI.

[5]  三嶋 博之.  The theory of affordances , 2008 .

[6]  Robin R. Murphy,et al.  Case studies of applying Gibson's ecological approach to mobile robots , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Liz Stinson.  The Speed of This , 2006 .

[8]  N Djemame,et al.  ARCHISIM: MULTI-ACTOR PARALLEL ARCHITECTURE FOR TRAFFIC SIMULATION , 1995 .

[9]  Neil A. Dodgson,et al.  Invariants and Affordances for Walking in a Cluttered Environment , 1999 .

[10]  Adrian R. Pearce,et al.  The human agent virtual environment , 2007, AAMAS '07.

[11]  Peter Willemsen,et al.  Steering behaviors for autonomous vehicles in virtual environments , 2005, IEEE Proceedings. VR 2005. Virtual Reality, 2005..

[12]  John W. Polak,et al.  New Approach to Modeling Mixed Traffic Containing Motorcycles in Urban Areas , 2009 .

[13]  Sylvain Piechowiak,et al.  Anticipation based on constraint processing in a multi-agent context , 2008, Autonomous Agents and Multi-Agent Systems.

[14]  Anthony G. Cohn,et al.  Qualitative Spatial Representation and Reasoning , 2008, Handbook of Knowledge Representation.

[15]  Barry G. Silverman,et al.  Affordance Theory for Improving the Rapid Generation, Composability, and Reusability of Synthetic Agents and Objects , 2003 .

[16]  Philippe Mathieu,et al.  Virtual Lanes Interest for Motorcycles Simulation , 2007 .

[17]  Shoji Matsumoto,et al.  THE SPEED, FLOW AND HEADWAY ANALYSES OF MOTORCYCLE TRAFFIC , 2005 .

[18]  Peter Vortisch,et al.  Microscopic Traffic Flow Simulator VISSIM , 2010 .

[19]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[20]  Donald A. Norman,et al.  Affordance, conventions, and design , 1999, INTR.

[21]  Peter Hidas,et al.  MODELLING LANE CHANGING AND MERGING IN MICROSCOPIC TRAFFIC SIMULATION , 2002 .