# Online Parameter Estimation for Human Driver Behavior Prediction

arXiv: https://arxiv.org/abs/2005.02597 · HTML: https://mlsanity.com/paper/2005.02597/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 6 of 6)

- [Reactive and Safe Road User Simulations using Neural Barrier Certificates](https://mlsanity.com/paper/2109.06689/) (2021)
- [A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering](https://mlsanity.com/paper/2108.12820/) (2021)
- [A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System](https://mlsanity.com/paper/2310.03303/) (2023)
- [An NCAP-like Safety Indicator for Self-Driving Cars](https://mlsanity.com/paper/2104.00859/) (2021)
- [A Utility Maximization Model of Pedestrian and Driver Interactions](https://mlsanity.com/paper/2110.11015/) (2021)
- [BaTCAVe: Trustworthy Explanations for Robot Behaviors](https://mlsanity.com/paper/2409.10733/) (2024)

## Abstract (arXiv)

Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive, and can capture nuanced behavior. However, they usually lack interpretability and sometimes exhibit unrealistic-even dangerous-behavior. Rule-based models are interpretable, and can be designed to guarantee "safe" behavior, but are less expressive due to their low number of parameters. In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories. We solve the online parameter estimation problem using particle filtering, and benchmark performance against rule-based and black-box driver models on two real world driving data sets. We evaluate the closeness of our driver model to ground truth data demonstration and also assess the safety of the resulting emergent driving behavior.