Abstract

The problem of driving an autonomous vehicle in normal traffic engages many areas of AI research and has substantial economic significance. We describe work in progress on a new approach to this problem that uses a decision-theoretic architecture using dynamic probabilistic networks. The architecture provides a sound solution to the problems of sensor noise, sensor failure, and uncertainty about the behavior of other vehicles and about the effects of one's own actions. We report on advances in the theory of inference and decision making in dynamic, partially observable domains. Our approach has been implemented in a simulation system, and the autonomous vehicle successfully negotiates a variety of difficult situations. 1 The BAT Project Several government agencies and corporations in Europe, Japan, and the US are currently undertaking research in IVHS (Intelligent Vehicle and Highway Systems) with the aim of substantially reducing congestion and accidents, which cost $500 billion/year...

Citations