ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation
As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and benef...
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| Vydáno v: | IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1620 - 1632 |
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ACM
27.10.2024
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| ISSN: | 2643-1572 |
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| Abstract | As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and beneficial for enhancing ADS safety and reliability. Existing cyber-physical system (CPS) root cause analysis techniques are mainly designed for drones and cannot handle the unique challenges introduced by more complex physical environments and deep learning models deployed in ADS. In this paper, we address the gap by offering a formal definition of ADS root cause analysis problem and introducing Rocas, a novel ADS root cause analysis framework featuring cyber-physical co-mutation. Our technique uniquely leverages both physical and cyber mutation that can precisely identify the accident-trigger entity and pinpoint the misconfiguration of the target ADS responsible for an accident. We further design a differential analysis to identify the responsible module to reduce search space for the misconfiguration. We study 12 categories of ADS accidents and demonstrate the effectiveness and efficiency of Rocas in narrowing down search space and pinpointing the misconfiguration. We also show detailed case studies on how the identified misconfiguration helps understand rationale behind accidents. |
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| AbstractList | As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is paramount and beneficial for enhancing ADS safety and reliability. Existing cyber-physical system (CPS) root cause analysis techniques are mainly designed for drones and cannot handle the unique challenges introduced by more complex physical environments and deep learning models deployed in ADS. In this paper, we address the gap by offering a formal definition of ADS root cause analysis problem and introducing Rocas, a novel ADS root cause analysis framework featuring cyber-physical co-mutation. Our technique uniquely leverages both physical and cyber mutation that can precisely identify the accident-trigger entity and pinpoint the misconfiguration of the target ADS responsible for an accident. We further design a differential analysis to identify the responsible module to reduce search space for the misconfiguration. We study 12 categories of ADS accidents and demonstrate the effectiveness and efficiency of Rocas in narrowing down search space and pinpointing the misconfiguration. We also show detailed case studies on how the identified misconfiguration helps understand rationale behind accidents. |
| Author | Choi, Hongjun Xu, Xiangzhe Shi, Qingkai Cheng, Zhiyuan Cheng, Siyuan Ye, Yapeng Zhang, Xiangyu Feng, Shiwei |
| Author_xml | – sequence: 1 givenname: Shiwei surname: Feng fullname: Feng, Shiwei email: feng292@purdue.edu organization: Purdue University,West Lafayette,USA – sequence: 2 givenname: Yapeng surname: Ye fullname: Ye, Yapeng email: ye203@purdue.edu organization: Purdue University,West Lafayette,USA – sequence: 3 givenname: Qingkai surname: Shi fullname: Shi, Qingkai email: qingkaishi@nju.edu.cn organization: Nanjing University,The State Key Laboratory for Novel Software Technology,Nanjing,China – sequence: 4 givenname: Zhiyuan surname: Cheng fullname: Cheng, Zhiyuan email: cheng443@purdue.edu organization: Purdue University,West Lafayette,USA – sequence: 5 givenname: Xiangzhe surname: Xu fullname: Xu, Xiangzhe email: xu1415@purdue.edu organization: Purdue University,West Lafayette,USA – sequence: 6 givenname: Siyuan surname: Cheng fullname: Cheng, Siyuan email: cheng535@purdue.edu organization: Purdue University,West Lafayette,USA – sequence: 7 givenname: Hongjun surname: Choi fullname: Choi, Hongjun email: hongjun@dgist.ac.kr organization: DGIST,Daegu,South Korea – sequence: 8 givenname: Xiangyu surname: Zhang fullname: Zhang, Xiangyu email: xyzhang@cs.purdue.edu organization: Purdue University,West Lafayette,USA |
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| Snippet | As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to... |
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| SubjectTerms | Accidents Autonomous vehicles Deep learning Drones Object recognition Reliability Root cause analysis Safety Software engineering Testing |
| Title | ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation |
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