Agent-based evaluation of driver heterogeneous behavior during safety-critical events

Heterogeneous driver behavior during safety-critical events is more complicated than normal driving situations and is difficult to capture by statistical models. This paper applies an agent-based reinforcement learning method to represent heterogeneous driving behavior for different drivers during safety-critical events. The naturalistic driving data of different drivers during safety-critical events are used in agent training. As an output of the Neuro-Fuzzy Actor Critic Reinforcement Learning (NFACRL) training technique, behavior rules are embedded in different agents to represent heterogeneous actions between drivers. The results show that the NFACRL is able to simulate naturalistic driver behavior and present heterogeneity.

[1]  David E. Goldberg,et al.  SIGNAL TIMING DETERMINATION USING GENETIC ALGORITHMS , 1992 .

[2]  Fei-Yue Wang,et al.  Artificial Societies for Integrated and Sustainable Development of Metropolitan Systems , 2004, IEEE Intell. Syst..

[3]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[4]  Kai Nagel,et al.  Multi-agent traffic simulation with CUDA , 2009, 2009 International Conference on High Performance Computing & Simulation.

[5]  Kalyanmoy Deb,et al.  Parallelization of binary and real-coded genetic algorithms on GPU using CUDA , 2010, IEEE Congress on Evolutionary Computation.

[6]  Alfredo Benso,et al.  GPU acceleration for statistical gene classification , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[7]  Fei-Yue Wang,et al.  Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications , 2010, IEEE Transactions on Intelligent Transportation Systems.

[8]  Zhirui Ye,et al.  Development and Evaluation of a Multi-Agent Based Neuro-Fuzzy Arterial Traffic Signal Control System , 2007 .

[9]  Paul Davidsson,et al.  An Analysis of Agent-Based Approaches to Transport Logistics , 2005 .

[10]  Michael G.H. Bell,et al.  Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing , 2004 .

[11]  F. Zhu,et al.  A Case Study of Evaluating Traffic Signal Control Systems Using Computational Experiments , 2011, IEEE Transactions on Intelligent Transportation Systems.

[12]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Wang Feiyue.  Concepts and Frameworks of Artificial Transportation Systems , 2004 .

[14]  Richard J. Hanowski,et al.  Driver Distraction in Commercial Vehicle Operations , 2009 .

[15]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[16]  Fenghua Zhu,et al.  DynaCAS: Computational Experiments and Decision Support for ITS , 2008, IEEE Intelligent Systems.

[17]  Tetsu Narumi,et al.  Overheads in Accelerating Molecular Dynamics Simulations with GPUs , 2008, 2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies.

[18]  Jonas Tölke,et al.  Implementation of a Lattice Boltzmann kernel using the Compute Unified Device Architecture developed by nVIDIA , 2009, Comput. Vis. Sci..

[19]  Kai Nagel,et al.  Parallel implementation of the TRANSIMS micro-simulation , 2001, Parallel Comput..

[20]  D. L. Gerlough,et al.  Traffic flow theory : a monograph , 1975 .

[21]  Lionel Jouffe,et al.  Fuzzy inference system learning by reinforcement methods , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[22]  Nicolas Lefebvre,et al.  MATSim-T , 2009, Multi-Agent Systems for Traffic and Transportation Engineering.

[23]  Bo Chen,et al.  A Review of the Applications of Agent Technology in Traffic and Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.

[24]  Kai Nagel,et al.  Using common graphics hardware for multi-agent traffic simulation with CUDA , 2009, SIMUTools 2009.

[25]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[26]  Ralf Schleiffer.  Intelligent agents in traffic and transportation , 2002 .

[27]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[28]  A. Schadschneider,et al.  Statistical physics of vehicular traffic and some related systems , 2000, cond-mat/0007053.

[29]  Cheng Chen,et al.  A Game-Engine-Based Platform for Modeling and Computing Artificial Transportation Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[30]  M. Lighthill,et al.  On kinematic waves I. Flood movement in long rivers , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[31]  Javier J. Sanchez-Medina,et al.  Traffic Signal Optimization in “La Almozara” District in Saragossa Under Congestion Conditions, Using Genetic Algorithms, Traffic Microsimulation, and Cluster Computing , 2010, IEEE Transactions on Intelligent Transportation Systems.

[32]  Fei-Yue Wang,et al.  Agent-Based Control for Networked Traffic Management Systems , 2005, IEEE Intell. Syst..

[33]  Shigeyoshi Tsutsui,et al.  Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study , 2009, GECCO '09.