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

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 te...

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Bibliographic Details
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
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Summary: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.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines10111101