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arXiv · 2301.10893

Predicting Parameters for Modeling Traffic Participants

Ahmadreza Moradipari·Sangjae Bae·Mahnoosh Alizadeh·Ehsan Moradi Pari·David Isele
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Abstract

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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Ahmadreza Moradipari, Sangjae Bae, Mahnoosh Alizadeh, Ehsan Moradi Pari, David Isele. 2023-01-26. Predicting Parameters for Modeling Traffic Participants. https://arxiv.org/abs/2301.10893

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