Efficient and Effective Generation of Test Cases for Pedestrian Detection - Search-based Software Testing of Baidu Apollo in SVL
Paper in proceeding, 2021

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.

Automotive Simulators

Evolutionary Algorithm

Pedestrian Detection

Search-Based Test Generation

Advanced Driver Assistance Systems

Author

Hamid Ebadi

Infotiv AB

Mahshid Helali Moghadam

RISE Research Institutes of Sweden

Markus Borg

RISE Research Institutes of Sweden

Gregory Gay

University of Gothenburg

Afonso Fontes

University of Gothenburg

Kasper Socha

RISE Research Institutes of Sweden

Proceedings - 3rd IEEE International Conference on Artificial Intelligence Testing, AITest 2021

103-110
978-1-6654-3481-2 (ISBN)

2021 IEEE International Conference on Artificial Intelligence Testing (AITest)
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Subject Categories

Software Engineering

Computer Science

DOI

10.1109/AITEST52744.2021.00030

More information

Latest update

10/23/2023