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.
Wed 30 OctDisplayed time zone: Pacific Time (US & Canada) change
13:30 - 15:00 | Autonomous SystemsResearch Papers / Journal-first Papers / Industry Showcase at Gardenia Chair(s): Qingkai Shi Nanjing University | ||
13:30 15mTalk | SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving Testing Research Papers An Guo Nanjing University, Yuan Zhou Nanyang Technological University, Haoxiang Tian Nanyang Technological University, Chunrong Fang Nanjing University, Yunjian Sun Nanjing University, Weisong Sun Nanyang Technological University, Xinyu Gao , Luu Anh Tuan Nanyang Technological University, Yang Liu Nanyang Technological University, Zhenyu Chen Nanjing University Pre-print | ||
13:45 15mTalk | Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems Research Papers | ||
14:00 15mTalk | In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators Industry Showcase Dmytro Humeniuk Polytechnique Montréal, Houssem Ben Braiek Sycodal, Thomas Reid Sycodal, Foutse Khomh Polytechnique Montréal | ||
14:15 15mTalk | LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models Research Papers Shuncheng Tang University of Science and Technology of China, Zhenya Zhang Kyushu University, Japan, Jixiang Zhou University of Science and Technology of China, Lei Wang National University of Defense Technology, Yuan Zhou Zhejiang Sci-Tech University, Yinxing Xue University of Science and Technology of China | ||
14:30 15mTalk | ROCAS: Root Cause Analysis of Autonomous Driving Accidents via Cyber-Physical Co-mutation Research Papers Shiwei Feng Purdue University, Yapeng Ye Purdue University, Qingkai Shi Nanjing University, Zhiyuan Cheng Purdue University, Xiangzhe Xu Purdue University, Siyuan Cheng Purdue University, Hongjun Choi DGIST, Xiangyu Zhang Purdue University | ||
14:45 15mTalk | The IDEA of Us: An Identity-Aware Architecture for Autonomous Systems Journal-first Papers Carlos Gavidia-Calderon The Alan Turing Institute, Anastasia Kordoni Lancaster University (UK), Amel Bennaceur The Open University, UK, Mark Levine Lancaster University, Bashar Nuseibeh The Open University, UK; Lero, University of Limerick, Ireland | ||