A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Towards Truly Agent-Based Traffic and Mobility Simulations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
A general method for human activity recognition in video
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
The BDI driver in a service city
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Strategic behavior in dynamic cities
Proceedings of the 2011 Summer Computer Simulation Conference
Strategic behavior in a living environment
Proceedings of the Winter Simulation Conference
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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.