Symbolic Traffic Scene Analysis Using Dynamic Belief Networks

An important task for progress in IVHS (Intelligent Vehicle Highway Systems) is the development of methods for real-time traffic scene analysis. All three major applications of IVHS ADIS (Advanced Driver Information Systems), ATMS (Advanced Traffic Management Systems), and AVCS (Automated Vehicle Control Systems) could benefit from accurate, high-level descriptions of traffic situations. For example, an ADIS and an ATMS could use information about traffic congestion and accidents to alert drivers or to direct vehicles to alternate routes. An ATMS also could analyze local traffic at intersections to identify those with higher risk of accidents. Finally, an AVCS would need information about the actions of neighboring vehicles and the condition of traffic lanes ahead to control an automated car moving along a freeway [6]. Our work on traffic scene analysis is one of many projects under the PATH (Partners for Advanced Transit and Highways) program of the University of California’s Institute for Transportation Studies. The overall aim of PATH is to determine how automation and advanced technology can increase highway capacity and improve safety. To this end, multi-disciplinary research groups at the University of California are studying a wide range of issues such as message exchange protocols between moving platoons1 [2], critical safety concerns in a fully automated highway system [1], and highway surveillance using machine vision [9]. Although simple sensor-based schemes can provide information about traffic densities and velocities, such information will be imprecise, since drivers are constantly accelerating and decelerating, changing lanes, drifting, and turning. Moreover, traffic con-