一句话：通过访谈与问卷揭示自动驾驶系统测试的7项实践和4类需求，并梳理研究与实践差距。
自动驾驶系统常将深度神经网络与逻辑模块结合，给软件测试带来新挑战。为厘清现有测试技术是否满足从业者需求，本文首次开展综合性研究：对10家自动驾驶公司的开发者进行半结构化访谈，并问卷调查100名相关开发者，系统分析后归纳出7项常见实践与4类新兴需求。作者进一步综述现有ADS测试文献，建立技术分类体系，并分析研究与工业需求之间的差距。最后提出未来方向，如开发测试约简技术以加速基于仿真的ADS测试，为软件工程研究者提供指引。
Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional software systems, autonomous driving systems (ADSs) often use deep neural networks in tandem with logic-based modules. This new paradigm poses unique challenges for software testing. Despite the recent development of new ADS testing techniques, it is not clear to what extent those techniques have addressed the needs of ADS practitioners. To fill this gap, we present the first comprehensive study to identify the current practices and needs of ADS testing. We conducted semi-structured interviews with developers from 10 autonomous driving companies and surveyed 100 developers who have worked on autonomous driving systems. A systematic analysis of the interview and survey data revealed 7 common practices and 4 emerging needs of autonomous driving testing. Through a comprehensive literature review, we developed a taxonomy of existing ADS testing techniques and analyzed the gap between ADS research and practitioners’ needs. Finally, we proposed several future directions for SE researchers, such as developing test reduction techniques to accelerate simulation-based ADS testing.
DOI 原文 ·
@inproceedings{paperbot6976,
title = {Testing of autonomous driving systems: where are we and where should we go?},
author = {Guannan Lou and Yao Deng and Xi Zheng and Mengshi Zhang and Tianyi Zhang},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549111}
}