Paper ID: 2301.10893

Predicting Parameters for Modeling Traffic Participants

Ahmadreza Moradipari, Sangjae Bae, Mahnoosh Alizadeh, Ehsan Moradi Pari, David Isele

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.

Submitted: Jan 26, 2023