Challenges of Data-Driven Simulation of Diverse and Consistent Human Driving Behaviors

Kalle Kujanpää, Daulet Baimukashev, Shibei Zhu, Shoaib Azam, Farzeen Munir, Gokhan Alcan, Ville Kyrki

Abstract: Building simulation environments for developing and testing autonomous vehicles necessitates that the simulators accurately model the statistical realism of the real-world environment, including the interaction with other vehicles driven by human drivers. To address this requirement, an accurate human behavior model is essential to incorporate the diversity and consistency of human driving behavior. We propose a mathematical framework for designing a data-driven simulation model that simulates human driving behavior more realistically than the currently used physics-based simulation models. Experiments conducted using the NGSIM dataset validate our hypothesis regarding the necessity of considering the complexity, diversity, and consistency of human driving behavior when aiming to develop realistic simulators.