Online Parameter Estimation for Human Driver Behavior Prediction
This work addresses the need for interpretable and safe driver models for autonomous vehicles, though it is incremental as it builds on existing models with parameter estimation.
The paper tackled the problem of predicting human driver behavior by applying online parameter estimation to the Intelligent Driver Model, achieving collision-free trajectories that closely match real-world driving data.
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.