TY - GEN
T1 - Search-Based Selection and Prioritization of Test Scenarios for Autonomous Driving Systems
AU - Lu, Chengjie
AU - Zhang, Huihui
AU - Yue, Tao
AU - Ali, Shaukat
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Violating the safety of autonomous driving systems (ADSs) could lead to fatal accidents. ADSs are complex, constantly-evolving and software-intensive systems. Testing an individual ADS is challenging and expensive on its own, and consequently testing its multiple versions (due to evolution) becomes much more costly. Thus, it is needed to develop approaches for selecting and prioritizing tests for newer versions of ADSs based on historical test execution data of their previous versions. To this end, we propose a multi-objective search-based approach for Selection and Prioritization of tEst sCenarios for auTonomous dRiving systEms (SPECTRE) to test newer versions of an ADS based on four optimization objectives, e.g., demand of a test scenario put on an ADS. We experimented with five commonly used multi-objective evolutionary algorithms and used a repository of 60,000 test scenarios. Among all the algorithms, IBEA achieved the best performance for solving all the optimization problems of varying complexity.
AB - Violating the safety of autonomous driving systems (ADSs) could lead to fatal accidents. ADSs are complex, constantly-evolving and software-intensive systems. Testing an individual ADS is challenging and expensive on its own, and consequently testing its multiple versions (due to evolution) becomes much more costly. Thus, it is needed to develop approaches for selecting and prioritizing tests for newer versions of ADSs based on historical test execution data of their previous versions. To this end, we propose a multi-objective search-based approach for Selection and Prioritization of tEst sCenarios for auTonomous dRiving systEms (SPECTRE) to test newer versions of an ADS based on four optimization objectives, e.g., demand of a test scenario put on an ADS. We experimented with five commonly used multi-objective evolutionary algorithms and used a repository of 60,000 test scenarios. Among all the algorithms, IBEA achieved the best performance for solving all the optimization problems of varying complexity.
KW - Autonomous driving
KW - Multi-objective search
KW - Test optimization
UR - https://www.scopus.com/pages/publications/85117157020
U2 - 10.1007/978-3-030-88106-1_4
DO - 10.1007/978-3-030-88106-1_4
M3 - 会议稿件
AN - SCOPUS:85117157020
SN - 9783030881054
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 55
BT - Search-Based Software Engineering - 13th International Symposium, SSBSE 2021, Proceedings
A2 - O’Reilly, Una-May
A2 - Devroey, Xavier
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Symposium on Search-Based Software Engineering, SSBSE 2021
Y2 - 11 October 2021 through 12 October 2021
ER -