MOSAT: finding safety violations of autonomous driving systems using multi-objective genetic algorithm-Reference-Cited by-同舟云学术

MOSAT: finding safety violations of autonomous driving systems using multi-objective genetic algorithm

Published:2022-11-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Author:

Tian Haoxiang¹,Jiang Yan¹,Wu Guoquan²,Yan Jiren¹,Wei Jun³,Chen Wei⁴,Li Shuo⁵,Ye Dan⁵

Affiliation:

1. Institute of Software at Chinese Academy of Sciences, China / University of Chinese Academy of Sciences, China

2. Institute of Software at Chinese Academy of Sciences, China / University of Chinese Academy of Sciences, China / University of Chinese Academy of Sciences Nanjing College, China / China Southern Power Grid, China

3. Institute of Software at Chinese Academy of Sciences, China / University of Chinese Academy of Sciences, China / University of Chinese Academy of Sciences Chongqing School, China

4. Institute of Software at Chinese Academy of Sciences, China / University of Chinese Academy of Sciences, China / University of Chinese Academy of Sciences Nanjing College, China

5. Institute of Software at Chinese Academy of Sciences, China / University of Chinese Academy of Sciences, China / Nanjing Institute of Software Technology, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

China Southern Power Grid Company

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3540250.3549100

Reference63 articles.

1. 2013. An open autonomous driving platform. https://github.com/ApolloAuto/apollo 2013. An open autonomous driving platform. https://github.com/ApolloAuto/apollo

2. 2019. SVLSimulator. https://www.svlsimulator.com/docs/ 2019. SVLSimulator. https://www.svlsimulator.com/docs/

3. 2022. NHTSA. https://www.nhtsa.gov/sites/nhtsa.gov/files/811731.pdf 2022. NHTSA. https://www.nhtsa.gov/sites/nhtsa.gov/files/811731.pdf

4. 2022. Pre-crash scenarios in NHTSA. https://www.nhtsa.gov/sites/nhtsa.gov/files/pre-crash_scenario_typology-final_pdf_version_5-2-07.pdf 2022. Pre-crash scenarios in NHTSA. https://www.nhtsa.gov/sites/nhtsa.gov/files/pre-crash_scenario_typology-final_pdf_version_5-2-07.pdf

5. 2022. SVL Simulator Sunset. https://www.svlsimulator.com/news/2022-01-20-svl-simulator-sunset/ 2022. SVL Simulator Sunset. https://www.svlsimulator.com/news/2022-01-20-svl-simulator-sunset/

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