一句话：对百度Apollo和Autoware的1.6万余次提交与499个缺陷进行实证研究，提出缺陷分类并总结16项发现。
自动驾驶汽车（AV）正逐步融入日常生活，其软件缺陷直接关系到安全性、鲁棒性与正确性，然而此前尚无针对AV缺陷特征的系统性研究。本文选取两个主流开源AV软件系统——百度Apollo与Autoware，对16,851次代码提交和499个缺陷进行实证分析，构建了包含13类根本原因、20种缺陷症状以及18类常受影响软件组件的分类体系。研究从中提炼出16项主要发现，并据此为软件缺陷检测、定位与修复的未来研究方向提出更广泛的启示。该工作填补了AV软件缺陷实证研究的空白，为提升AV系统的可靠性提供了数据基础与分类框架。
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.
@inproceedings{paperbot5587,
title = {A comprehensive study of autonomous vehicle bugs},
author = {Joshua Garcia and Yang Feng and Junjie Shen and Sumaya Almanee and Yuan Xia and and Qi Alfred Chen},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380397}
}