Predicting Parameters for Modeling Traffic Participants

TL;DR

This paper introduces a method to predict traffic participant behaviors accurately using minimal observational data, enhancing autonomous vehicle navigation safety and efficiency.

Contribution

It presents a novel approach based on the intelligent driver model that predicts driver behavior from limited data, outperforming some existing data-driven methods.

Findings

Prediction errors less than 1 meter over 10 seconds

Effective modeling with few observable features

Outperforms some reinforcement learning approaches

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.