Learning naturalistic driving environment with statistical realism

Xintao Yan, Zhengxia Zou, Shuo Feng, Haojie Zhu, Haowei Sun and Henry X. Liu ()
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Xintao Yan: University of Michigan
Zhengxia Zou: University of Michigan
Shuo Feng: University of Michigan
Haojie Zhu: University of Michigan
Haowei Sun: University of Michigan
Henry X. Liu: University of Michigan

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations.

Date: 2023
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DOI: 10.1038/s41467-023-37677-5

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