Comparing two systematic approaches for testing automated driving functions

Felbinger, Hermann; Klück, Florian; Li, Yihao; Nica, Mihai; Tao, Jianbo; Wotawa, Franz; Zimmermann, Martin

doi:10.1109/iccve45908.2019.8965209

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Starting from more promising conditions, the optimizer converges faster to the global optimum, or perhaps finds a better local optimum. Felbinger et al [144] compare falsification and combinatorial testing for an Autonomous Emergency Braking (AEB) System. Their results show that both methods have been proven to find critical scenarios, but the authors do not assess the efficiency.…”

Section: Simulation-based Falsificationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

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Section: Simulation-based Falsificationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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“…Kluck et al [7] proposed an approach for test parameter optimization using genetic algorithms and have employed it for testing an autonomous emergency braking function. Felbinger et al [46] compared a genetic algorithm approach and a combinatorial testing approach for detecting critical scenarios for an emergency braking function.…”

Section: Search-based Approachesmentioning

confidence: 99%

Critical Scenario Identification for Realistic Testing of Autonomous Driving Systems

Song

Tan

Runeson

et al. 2023

Preprint

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Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial realworld testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical. Thus, critical ones have to be prioritized. We have developed for critical test scenario identification and in this study, we implement the approach and validate it on two real autonomous driving systems from industry by integrating it into their tool-chain. Our main contribution in this work is the demonstration and validation of our approach for critical scenario identification for testing real autonomous driving systems.

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“…Klück et al (2019) proposed an approach for test parameter optimization using genetic algorithms and have employed it for testing an autonomous emergency braking function. Felbinger et al (2019) compared a genetic algorithm approach and a combinatorial testing approach for detecting critical scenarios for an emergency braking function.…”

Section: Search-based Approachesmentioning

confidence: 99%

Critical scenario identification for realistic testing of autonomous driving systems

et al. 2022

View full text Add to dashboard Cite

Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial real-world testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical. Thus, critical ones have to be prioritized. We have developed an approach for critical test scenario identification and in this study, we implement the approach and validate it on two real autonomous driving systems from industry by integrating it into their tool-chain. Our main contribution in this work is the demonstration and validation of our approach for critical scenario identification for testing real autonomous driving systems.

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Comparing two systematic approaches for testing automated driving functions

Cited by 9 publications

References 8 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Critical Scenario Identification for Realistic Testing of Autonomous Driving Systems

Critical scenario identification for realistic testing of autonomous driving systems

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