Research on intelligent driving simulation scenario generation and instantiation method

Manman Cao; Shuo Chen; Shuai Zhao; Pengchao Zhao

doi:10.1117/12.2685322

21 July 2023 Research on intelligent driving simulation scenario generation and instantiation method

Manman Cao, Shuo Chen, Shuai Zhao, Pengchao Zhao

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170Z (2023) https://doi.org/10.1117/12.2685322
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

In order to better conform to the real driving scene, intelligent driving simulation test needs to generalize the test scene on the basis of the collected natural driving data. In this context, a scenario generalization method for intelligent driving simulation based on Latin hypercube sampling is proposed in this paper, which makes up for the defect of the traditional Monte Carlo scenario generalization, in which the scene parameters are not covered enough in each dimension and ensures the coverage of the generalized scene. The comparison experiment shows that the generalization method of intelligent driving simulation scenarios based on Latin hypercube sampling can be applied in a limited time. Almost all scenarios are covered within the sampling times. Under the condition that the sampling times are the same, the risk degree of simulated scenarios can be guaranteed to be consistent with the real data sources and have higher robustness.

Citation Download Citation

Manman Cao, Shuo Chen, Shuai Zhao, and Pengchao Zhao "Research on intelligent driving simulation scenario generation and instantiation method", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170Z (21 July 2023); https://doi.org/10.1117/12.2685322

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