A Comprehensive Study of Autonomous Vehicle Bugs
A Comprehensive Study of Autonomous Vehicle Bugs
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DOI：
10.1145/3377811.3380397
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Joshua Garcia;Yang Feng;Junjie Shen;Sumaya Almanee;Yuan Xia;Qi Alfred Chen
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文献类型：
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作者：
Joshua Garcia;Yang Feng;Junjie Shen;Sumaya Almanee;Yuan Xia;Qi Alfred Chen
自动驾驶汽车或自动驾驶汽车（AV）越来越成为我们日常生活中不可或缺的一部分。大约有50家公司正在积极从事AV，包括Google，Ford和Intel等大型公司。一些AV已经在公共道路上运行，最近至少有一场不幸的死亡记录在案。结果，了解AV中的错误对于确保其安全性，安全性，稳健性和正确性至关重要。尽管以前的研究集中在各种域（例如数值软件；机器学习；以及错误处理，并发和性能错误）以研究错误特征，但尚未以类似的方式研究AVS。最近，开源社区中出现了两个针对AVS的软件系统，Baidu Apollo和Autoware已成为领先者，并已被大型公司和政府（例如Lincoln，Volvo，Volvo，Ford，Intel，Intel，Hitachi，Hitachi，美国LG和美国）使用运输部）。从这两个领先的AV软件系统中，本文描述了我们对16,851个提交和499个AV错误的调查，并将这些错误的分类介绍为13个根本原因，20个错误症状和18种类别的软件组件，这些错误通常会影响这些错误。我们从我们的研究中确定了16个主要发现，并从中汲取了更广泛的教训，以指导研究社区向未来的软件错误检测，本地化和维修方向指导。
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.