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Predicting Parameters for Modeling Traffic Participants

2023-01-26 01:34:17
Ahmadreza Moradipari, Sangjae Bae, Mahnoosh Alizadeh, Ehsan Moradi Pari, David Isele

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.

Abstract (translated)

准确建模交通参与者的行为对于在繁忙的交通中安全高效地引导自动驾驶车辆至关重要。我们提出了一种基于智能司机模型的方法,该方法仅使用可观测的特征来准确建模个体司机的行为。通常情况下,该方法会导致在高速公路驾驶的10秒时间窗口内的预测误差与全信息oracle相差不到1米。随后,我们通过对竞争数据驱动方法如强化学习进行广泛分析来验证我们的方法的效率。

URL

https://arxiv.org/abs/2301.10893

PDF

https://arxiv.org/pdf/2301.10893.pdf


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