{"title":"ROCAS:通过网络-物理协同突变分析自动驾驶事故的根本原因","authors":"Shiwei Feng, Yapeng Ye, Qingkai Shi, Zhiyuan Cheng, Xiangzhe Xu, Siyuan Cheng, Hongjun Choi, Xiangyu Zhang","doi":"arxiv-2409.07774","DOIUrl":null,"url":null,"abstract":"As Autonomous driving systems (ADS) have transformed our daily life, safety\nof ADS is of growing significance. While various testing approaches have\nemerged to enhance the ADS reliability, a crucial gap remains in understanding\nthe accidents causes. Such post-accident analysis is paramount and beneficial\nfor enhancing ADS safety and reliability. Existing cyber-physical system (CPS)\nroot cause analysis techniques are mainly designed for drones and cannot handle\nthe unique challenges introduced by more complex physical environments and deep\nlearning models deployed in ADS. In this paper, we address the gap by offering\na formal definition of ADS root cause analysis problem and introducing ROCAS, a\nnovel ADS root cause analysis framework featuring cyber-physical co-mutation.\nOur technique uniquely leverages both physical and cyber mutation that can\nprecisely identify the accident-trigger entity and pinpoint the\nmisconfiguration of the target ADS responsible for an accident. We further\ndesign a differential analysis to identify the responsible module to reduce\nsearch space for the misconfiguration. We study 12 categories of ADS accidents\nand demonstrate the effectiveness and efficiency of ROCAS in narrowing down\nsearch space and pinpointing the misconfiguration. We also show detailed case\nstudies on how the identified misconfiguration helps understand rationale\nbehind accidents.","PeriodicalId":501278,"journal":{"name":"arXiv - CS - Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation\",\"authors\":\"Shiwei Feng, Yapeng Ye, Qingkai Shi, Zhiyuan Cheng, Xiangzhe Xu, Siyuan Cheng, Hongjun Choi, Xiangyu Zhang\",\"doi\":\"arxiv-2409.07774\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As Autonomous driving systems (ADS) have transformed our daily life, safety\\nof ADS is of growing significance. While various testing approaches have\\nemerged to enhance the ADS reliability, a crucial gap remains in understanding\\nthe accidents causes. Such post-accident analysis is paramount and beneficial\\nfor enhancing ADS safety and reliability. Existing cyber-physical system (CPS)\\nroot cause analysis techniques are mainly designed for drones and cannot handle\\nthe unique challenges introduced by more complex physical environments and deep\\nlearning models deployed in ADS. In this paper, we address the gap by offering\\na formal definition of ADS root cause analysis problem and introducing ROCAS, a\\nnovel ADS root cause analysis framework featuring cyber-physical co-mutation.\\nOur technique uniquely leverages both physical and cyber mutation that can\\nprecisely identify the accident-trigger entity and pinpoint the\\nmisconfiguration of the target ADS responsible for an accident. We further\\ndesign a differential analysis to identify the responsible module to reduce\\nsearch space for the misconfiguration. We study 12 categories of ADS accidents\\nand demonstrate the effectiveness and efficiency of ROCAS in narrowing down\\nsearch space and pinpointing the misconfiguration. We also show detailed case\\nstudies on how the identified misconfiguration helps understand rationale\\nbehind accidents.\",\"PeriodicalId\":501278,\"journal\":{\"name\":\"arXiv - CS - Software Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Software Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.07774\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.