A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing

• Published in: Machines (Basel) 2022-11, Vol.10 (11), p.1101
• Main Authors: Cai, Jinkang, Deng, Weiwen, Guang, Haoran, Wang, Ying, Li, Jiangkun, Ding, Juan
• Format: Article
• Language: English
• Subjects: automated vehicles; Automation; Concrete; Data collection; Driving; Knowledge; Methods; Scenario generation; scenario-based testing; Surveys; Traffic accidents; Traffic Safety; Vehicles
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines10111101

Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.