面向自动驾驶的仿真场景自动生成方法综述

随着自动驾驶测试验证对虚拟仿真场景依赖程度的增加,传统基于专家经验的场景枚举生成方法已无法满足测试需求.数字虚拟仿真场景自动生成方法在场景多样性、危险性、可解释性、生成效率等方面存在巨大技术优势,是提高汽车自动驾驶技术测试验证安全性和可靠性的关键,已成为当前汽车智能化领域的研究热点.在广泛调研场景自动生成方法领域研究成...

A Survey on Automatic Simulation Scenario Generation Methods for Autonomous Driving

The traditional scenario enumeration method based on expert experience has failed to meet testing requirements owing to the increasing reliance of autonomous driving on virtual simulation scenarios for testing and verification. The automatic generation of simulation scenarios has substantial technical advantages in terms of scenario diversity, safety, interpretability, and generation efficiency. It plays a crucial role in improving the efficiency of autonomous driving tests, which have become a prevalent research topic. In recent years, researchers have intensively studied automatic scenario generation methods. In the present study, extensive research was conducted on the results obtained in the field of automatic scenario generation. Thus, the latest research progress in scenario definition, scenario deconstruction, scenario generation based on mechanism modeling, scenario generation driven by data, etc., is schematically presented in this paper. In addition, an analysis on some areas worthy of further study was performed, and prospective research directions are presented herein. In terms of scenario deconstruction, given that scenarios are abundant, extremely complex, and inexhaustible, substantial importance should be given to research on the deconstruction of heterogeneous complex scenarios with the coupling of "field-weather-traffic." Regarding mechanism modeling, to meet the requirements of testing scenario diversity and boundary generation, the focus should be on scenario combination generation, edge scenario optimization generation, and adaptive generation. Furthermore, data with rich content must be collected, laying the foundation for research. To fully exploit the test value of scenario data, attention should be paid to the research on scenario reconstruction, thereby accelerating the generation of test scenario databases and dangerous scenarios. Thus, future research should focus on the aspects mentioned above to establish a completely automatic simulation scenario generation system for autonomous driving. This will lay a theoretical foundation for performing large-scale simulation tests of high-level autonomous driving.