Predicting Parameters for Modeling Traffic Participants.
International Conference on Intelligent Transportation Systems (ITSC)(2022)
摘要
Accurately modeling the behavior of traffic participants is essential for safely and efficiently navigating an autonomous vehicle through heavy traffic. We propose a method, based on the intelligent driver model, that allows us to accurately model individual driver behaviors from only a small number of frames using easily observable features. On average, this method makes prediction errors that have less than 1 meter difference from an oracle with full-information when analyzed over a 10-second horizon of highway driving. We then validate the efficiency of our method through extensive analysis against a competitive data-driven method such as Reinforcement Learning that may be of independent interest.
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关键词
autonomous vehicle,heavy traffic,intelligent driver model,individual driver behaviors,prediction errors,competitive data-driven method,parameter prediction,traffic participant modeling,reinforcement learning,time 10 s
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