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Artificial transportation systems (ATS) are an extension of traffic simulations that deal with transportation issues from the complex systems perspective in a systematic and synthetic way. A rule-based iterative ATS design process is presented in this paper, together with a prototype based on the multiagent platform-Swarm and the methods and results of computational experiments conducted on it. Both...
Automatic mining of vehicle behaviors from raw data collected by multiple sensors provides meaningful qualitative descriptions of the vehicle status. These qualitative behavior descriptions can be used in scenario parsing and have further applications in vehicle surveillance and frontal collision warning systems. In current approaches, the number of behavior categories is supposed to be known, or...
Tracking vehicles in heavy-traffic video is a challenging problem. It is hard for algorithms based on background extraction to work well. We propose an algorithm that does not need background information. In this algorithm, vehicle corners are detected and tracked, and then grouped into vehicles. Experiments show the effectiveness of the proposed algorithm under heavy congestion.
In most present frontal collision warning systems (FCWS), the warning algorithm mainly depends on simple combination of linearly predicted vehicle-motion parameters. Such systems suffer from high false-alarm-rate due to the incapability of identifying different transportation scenarios. Scenario parsing deals with such problems by analyzing transportation scenarios and applying specific threat assessment...
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