Evolving an intelligent vehicle for tactical reasoning in traffic

Recent research in automated highway systems has ranged from low-level vision-based controllers to high-level route-guidance software. However there is currently no system for tactical-level reasoning. Such a system should address tasks such as passing cars, making exits on time, and merging into a traffic stream. Our approach to this intermediate-level planning combines a distributed reasoning system (PolySAPIENT) with a novel evolutionary optimization strategy (PBIL). PBIL automatically tunes PolySAPIENT module parameters in simulation by evaluating candidate modules on various traffic scenarios. Since the control interface to the simulated vehicles is identical to that on the Carnegie Mellon Navlab vehicles, modules developed using this process can be directly ported to existing hardware. This method is currently being applied to the automated highway system domain; it also generalizes to many complex robotics tasks where multiple interacting modules must simultaneously be configured without individual module feedback.

[1]  J. H. Rillings,et al.  Advanced driver information systems , 1990 .

[2]  Larry J. Eshelman The CHC Adaptive Search Algo-rithm , 1991 .

[3]  James F. Cremer,et al.  The Software Architecture for Scenario Control in the Iowa Driving Simulator , 1993 .

[4]  Steven A. Shafer,et al.  Selective Perception for Robot Driving , 1993, AAAI.


[6]  Dean A. Pomerleau,et al.  Neural Network Perception for Mobile Robot Guidance , 1993 .

[7]  I. Masaki Vision-based vehicle guidance , 1992, Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation.

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  E. D. Dickmanns,et al.  A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision , 1987, Other Conferences.

[11]  Charles E. Thorpe,et al.  Tactical-level simulation for intelligent transportation systems , 1998 .

[12]  Martial Hebert,et al.  Vision and navigation for the Carnegie-Mellon Navlab , 1988 .

[13]  Ronald C. Arkin,et al.  Using Genetic Algorithms to Learn Reactive Control Parameters for Autonomous Robotic Navigation , 1994, Adapt. Behav..

[14]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[15]  John A. Michon,et al.  A critical view of driver behavior models: What do we know , 1985 .