A Survey on Automated Driving System Testing: Landscapes and Trends

Shuncheng Tang; Zhenya Zhang; Yi Zhang; Jixiang Zhou; Yan Guo; Shuang Liu; Shengjian Guo; Yan-Fu Li; Lei Ma; Yinxing Xue; Yang Liu

arXiv · 2206.05961

A Survey on Automated Driving System Testing: Landscapes and Trends

Shuncheng Tang·Zhenya Zhang·Yi Zhang·Jixiang Zhou·Yan Guo·Shuang Liu·Shengjian Guo·Yan-Fu Li·Lei Ma·Yinxing Xue·Yang Liu

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Abstract

Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning, and control, which brings together the latest advances in different domains. Despite these achievements, safety assurance of ADS is of great significance, since unsafe behavior of ADS can bring catastrophic consequences. Testing has been recognized as an important system validation approach that aims to expose unsafe system behavior; however, in the context of ADS, it is extremely challenging to devise effective testing techniques, due to the high complexity and multidisciplinarity of the systems. There has been great much literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. Most of the surveys focus on the system-level testing performed within software simulators, and they thereby ignore the distinct features of different modules. In this paper, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) we survey the module-level testing techniques for ADS and highlight the technical differences affected by the features of different modules; (2) we also survey the system-level testing techniques, with focuses on the empirical studies that summarize the issues occurring in system development or deployment, the problems due to the collaborations between different modules, and the gap between ADS testing in simulators and the real world; (3) we identify the challenges and opportunities in ADS testing, which pave the path to the future research in this field.

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Shuncheng Tang, Zhenya Zhang, Yi Zhang, Jixiang Zhou, Yan Guo, Shuang Liu, Shengjian Guo, Yan-Fu Li, Lei Ma, Yinxing Xue, Yang Liu. 2023-01-14. A Survey on Automated Driving System Testing: Landscapes and Trends. https://arxiv.org/abs/2206.05961

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