Research on intelligent driving simulation scenario generation and instantiation method

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.