A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

TL;DR

This paper introduces a hybrid driver modeling approach combining rule-based and data-driven methods, using particle filtering to learn interpretable parameters from real driving data, resulting in realistic traffic simulation.

Contribution

It presents a novel hybrid methodology that integrates rule-based models with online data-driven parameter learning via particle filtering for driver behavior modeling.

Findings

Accurately captures real-world driving behavior

Generates realistic traffic simulations

Humans struggle to distinguish real from model-generated driving videos

Abstract

Autonomous vehicles need to model the behavior of surrounding human driven vehicles to be safe and efficient traffic participants. Existing approaches to modeling human driving behavior have relied on both data-driven and rule-based methods. While data-driven models are more expressive, rule-based models are interpretable, which is an important requirement for safety-critical domains like driving. However, rule-based models are not sufficiently representative of data, and data-driven models are yet unable to generate realistic traffic simulation due to unrealistic driving behavior such as collisions. In this paper, we propose a methodology that combines rule-based modeling with data-driven learning. While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering. We perform driver modeling experiments on the task of highway driving and merging using data from three real-world driving demonstration datasets. Our results show that driver models based on our hybrid rule-based and data-driven approach can accurately capture real-world driving behavior. Further, we assess the realism of the driving behavior generated by our model by having humans perform a driving Turing test, where they are asked to distinguish between videos of real driving and those generated using our driver models.