# Predicting Parameters for Modeling Traffic Participants

arXiv: https://arxiv.org/abs/2301.10893 · HTML: https://mlsanity.com/paper/2301.10893/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2023.

## Built on by (most cited first, 3 of 3)

- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [RCMS: Risk-Aware Crash Mitigation System for Autonomous Vehicles](https://mlsanity.com/paper/2309.12531/) (2023)
- [On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning](https://mlsanity.com/paper/2604.07944/) (2026)

## Abstract (arXiv)

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.