Decision-theoretic reasoning for traffic monitoring and vehicle control

We describe technology for robust traffic monitoring and automated vehicle control using decision-theoretic and probabilistic reasoning methods. In this work, we have designed and implemented probabilistic models for deriving high-level descriptions of traffic conditions, as well as the maneuvers and intentions of individual vehicles, from visual observation of a traffic scene. Enhancements to standard probabilistic modeling and inference techniques have improved the performance of uncertain reasoning over time with continuous variables. We have demonstrated our models and algorithms in real-time analysis of traffic images as well as control of simulated vehicles