Challenges of Data-Driven Simulation of Diverse and Consistent Human Driving Behaviors
CoRR(2024)
摘要
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.
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