Towards a behavioural traffic monitoring system

In recent years, traffic video surveillance has increased significantly. However, most of the footage is reviewed by humans or not at all. Therefore, tools capable of analysing traffic video sequences and autonomously extracting information are required. In this paper, we present an agent-based approach to analysing driver behaviour. Our work differs from normal road monitoring systems in that we are interested in inferences about driver behaviour and in learning normal' driving modes, rather than specific instances of driver actions. Our system provides a behavioural description of traffic scenes. First, we present a kinematic traffic simulator designed to test driving agents. The simulator supports multiple lanes, obstructions and different environmental conditions. Second, we specify the agent's perception and reasoning models. Contrary to current autonomous driving systems, our behavioural models primarily influence agent perception. This approach is supported by recent psychological studies carried out on human drivers. Furthermore it simplifies the system implementation, increasing the ease of testing alternative models. By embedding the agents in the simulator, we observe classical traffic behaviour. Finally, we suggest ways to use the system's results directly or within higher level tools.