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Efficient and Effec...
Efficient and Effective Generation of Test Cases for Pedestrian Detection - Search-based Software Testing of Baidu Apollo in SVL
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- Ebadi, Hamid (författare)
- Infotiv AB,Gothenburg,Sweden
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- Helali Moghadam, Mahshid (författare)
- RISE,Industriella system,RISE Research Institutes of Sweden, Västerås, Sweden
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- Borg, Markus (författare)
- RISE,Mobilitet och system,RISE Research Institutes of Sweden, Västerås, Sweden
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- Gay, Gregory, 1987 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Institutionen för data- och informationsteknik, Software Engineering (GU),Department of Computer Science and Engineering (GU),Institutionen för data- och informationsteknik, Software Engineering (GU)
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- Fontes, Afonso, 1987 (författare)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Institutionen för data- och informationsteknik, Software Engineering (GU),Department of Computer Science and Engineering (GU),Institutionen för data- och informationsteknik, Software Engineering (GU)
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- Socha, Kasper (författare)
- RISE,Mobilitet och system,RISE Research Institutes of Sweden, Lund, Sweden
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: 2021 IEEE International Conference on Artificial Intelligence Testing (AITest). - : IEEE. - 9781665434812 ; , s. 103-110
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Abstract
Ämnesord
Stäng
- With the growing capabilities of autonomous vehicles, there is a higher demand for sophisticated and pragmatic quality assurance approaches for machine learning-enabled systems in the automotive AI context. The use of simulation-based prototyping platforms provides the possibility for early-stage testing, enabling inexpensive testing and the ability to capture critical corner-case test scenarios. Simulation-based testing properly complements conventional on-road testing. However, due to the large space of test input parameters in these systems, the efficient generation of effective test scenarios leading to the unveiling of failures is a challenge. This paper presents a study on testing pedestrian detection and emergency braking system of the Baidu Apollo autonomous driving platform within the SVL simulator. We propose an evolutionary automated test generation technique that generates failure-revealing scenarios for Apollo in the SVL environment. Our approach models the input space using a generic and flexible data structure and benefits a multi-criteria safety-based heuristic for the objective function targeted for optimization. This paper presents the results of our proposed test generation technique in the 2021 IEEE Autonomous Driving AI Test Challenge. In order to demonstrate the efficiency and effectiveness of our approach, we also report the results from a baseline random generation technique. Our evaluation shows that the proposed evolutionary test case generator is more effective at generating failure-revealing test cases and provides higher diversity between the generated failures than the random baseline.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Software testing
- Quality assurance
- Web and internet services
- Software algorithms
- Test pattern generators
- Artificial intelligence
- Autonomous vehicles
- Search-Based Test Generation
- Evolutionary Algorithm
- Advanced Driver Assistance Systems
- Pedestrian Detection
- Automotive Simulators
- Advanced Driver Assistance Systems
- Automotive Simulators
- Evolutionary Algorithm
- Pedestrian Detection
- Search-Based Test Generation
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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