Adaptive Intelligent Vehicle Modules for Tactical Driving

Intelligent vehicles must make real-time tactical level decisions to drive in mixed traffic environments. SAPIENT is a reasoning system that combines high-level task goals with low-level sensor constraints to control simulated and (ultimately) real vehicles like the Carnegie Mellon Navlab robot vans. SAPIENT consists of a number of reasoning modules whose outputs are combined using a voting scheme. The behavior of these modules is directly dependent on a large number of parameters both internal and external to the modules. Without carefully setting these parameters, it is difficult to assess whether the reasoning modules can interact correctly; furthermore, selecting good values for these parameters manually is tedious and error-prone. We use an evolutionary algorithm, termed Population-Based Incremental Learning, to automatically set each module’s parameters. This allows us to determine whether the combination of chosen modules is well suited for the desired task, enables the rapid integration of new modules into existing SAPIENT configurations, and provides a painless way to find good parameter settings.

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

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

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

[4]  Dean A. Pomerleau,et al.  SHIVA: Simulated Highways for Intelligent Vehicle Algorithms , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

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

[6]  Rahul Sukthankar,et al.  RACCOON: A Real-time Autonomous Car Chaser Operating Optimally At Night , 1993, Proceedings of the Intelligent Vehicles '93 Symposium.

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

[8]  Ashwin Ram,et al.  A Multistrategy Case-Based and Reinforcement Learning Approach to Self-Improving Reactive Control Systems for Autonomous Robotic Navigation , 1993 .

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

[10]  Juan Carlos,et al.  Multistrategy Learning in a Reactive Control Systems for Autonomous Robotic Navigation , 1993, Informatica.

[11]  Dean A. Pomerleau,et al.  Simulated Highways for Intelligent Vehicle Algorithms , 1995 .

[12]  C..  Vision-based Vehicle Guidance , 1992, Springer Series in Perception Engineering.

[13]  Rahul Sukthankar,et al.  Prototyping Intelligent Vehicle Modules Using Evolutionary Algorithms , 1997 .

[14]  Charles E. Thorpe,et al.  A Simulation and Design System for Tactical Driving Algorithms , 1996 .

[15]  Rahul Sukthankar,et al.  Situational Awareness for Driving in Traffic A Thesis Proposal , 1994 .

[16]  S. Baluja.  An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics , 1995 .

[17]  Takeo Kanade,et al.  Vision and Navigation for the Carnegie-Mellon Navlab , 1987 .

[18]  Keith Gardels.  AUTOMATIC CAR CONTROLS FOR ELECTRONIC HIGHWAYS , 1960 .

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

[20]  R. von Tomkewitsch,et al.  Dynamic route guidance and interactive transport management with ALI-SCOUT , 1991 .

[21]  Takeo Kanade,et al.  First Results in Robot Road-Following , 1985, IJCAI.

[22]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[23]  Dean A. Pomerleau,et al.  Vision guided lane transition , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[24]  Dean A. Pomerleau,et al.  PANS: a portable navigation platform , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.