Self-driving cars, or Autonomous Vehicles (AVs), are increasingly becoming an integral part of our daily life. About 50 corporations are actively working on AVs, including large companies such as Google, Ford, and Intel. Some AVs are already operating on public roads, with at least one unfortunate fatality recently on record. As a result, understanding bugs in AVs is critical for ensuring their security, safety, robustness, and correctness. While previous studies have focused on a variety of domains (e.g., numerical software; machine learning; and error-handling, concurrency, and performance bugs) to investigate bug characteristics, AVs have not been studied in a similar manner. Recently, two software systems for AVs, Baidu Apollo and Autoware, have emerged as frontrunners in the open-source community and have been used by large companies and governments (e.g., Lincoln, Volvo, Ford, Intel, Hitachi, LG, and the US Department of Transportation). From these two leading AV software systems, this paper describes our investigation of 16,851 commits and 499 AV bugs and introduces our classification of those bugs into 13 root causes, 20 bug symptoms, and 18 categories of software components those bugs often affect. We identify 16 major findings from our study and draw broader lessons from them to guide the research community towards future directions in software bug detection, localization, and repair.
Tue 7 JulDisplayed time zone: (UTC) Coordinated Universal Time change
15:00 - 16:00 | A1-Autonomous Driving SystemsTechnical Papers at Baekje Chair(s): Donghwan Shin University of Luxembourg (SnT) | ||
15:00 12mTalk | SLEMI: Equivalence Modulo Input (EMI) Based Mutation of CPS Models for Finding Compiler Bugs in Simulink Technical Papers Shafiul Azam Chowdhury University of Texas at Arlington, Sohil Lal Shrestha The University of Texas at Arlington, Taylor T Johnson Vanderbilt University, Christoph Csallner University of Texas at Arlington Link to publication DOI Media Attached | ||
15:12 12mTalk | DeepBillboard: Systematic Physical-World Testing of Autonomous Driving SystemsTechnical Technical Papers Husheng Zhou The University of Texas at Dallas, Wei Li Southern University of Science and Technology, Zelun Kong The University of Texas at Dallas, Junfeng Guo The University of Texas at Dallas, Yuqun Zhang Southern University of Science and Technology, Lingming Zhang The University of Texas at Dallas, Bei Yu The Chinese University of Hong Kong, Cong Liu UT Dallas | ||
15:24 12mTalk | Misbehaviour Prediction for Autonomous Driving SystemsTechnical Technical Papers Andrea Stocco Università della Svizzera italiana, Michael Weiss Università della Svizzera Italiana (USI), Marco Calzana Università della Svizzera Italiana (USI), Paolo Tonella Università della Svizzera italiana Pre-print | ||
15:36 12mTalk | Approximation-Refinement Testing of Compute-Intensive Cyber-Physical Models: An Approach Based on System Identification Technical Papers Claudio Menghi University of Luxembourg, Shiva Nejati University of Ottawa, Lionel Briand SnT Centre/University of Luxembourg, Yago Isasi Parache LuxSpace | ||
15:48 12mTalk | A Comprehensive Study of Autonomous Vehicle BugsTechnical Technical Papers Joshua Garcia University of California, Irvine, Yang Feng Nanjing University, Junjie Shen University of California, Irvine, Sumaya Almanee University of California, Irvine, Yuan Xia University of California, Irvine, Qi Alfred Chen University of California, Irvine | ||