一句话：将先进证伪器用于 OpenPilot 的 DNN，揭示真实自动驾驶网络证伪的挑战、工程适配与经验。
OpenPilot 通过深度神经网络（DNN）实现车道居中和自适应巡航等功能，但这类网络可能违反安全属性并导致碰撞。为在部署前发现潜在违反，本文研究证伪（falsification）——一种有向测试，通过分析 DNN 生成能触发安全属性违反的输入。作者将一种最先进的证伪器应用于 OpenPilot 中反映近期设计趋势的 DNN，报告了将其用于真实世界网络时遇到的挑战，描述了为克服这些挑战所做的工程适配，并展示了证伪器检测属性违反、提供有意义反例的潜力。最后总结了经验教训以及证伪器要在 OpenPilot 这类系统上发挥潜力仍待解决的问题。
Openpilot is an open source system to assist drivers by providing features like automated lane centering and adaptive cruise control. Like most systems for autonomous vehicles, Openpilot relies on a sophisticated deep neural network (DNN) to provide its functionality, one that is susceptible to safety property violations that can lead to crashes. To uncover such potential violations before deployment, we investigate the use of falsification, a form of directed testing that analyzes a DNN to generate an input that will cause a safety property violation. Specifically, we explore the application of a state-of-the-art falsifier to the DNN used in OpenPilot, which reflects recent trends in network design. Our investigation reveals the challenges in applying such falsifiers to real-world DNNs, conveys our engineering efforts to overcome such challenges, and showcases the potential of falsifiers to detect property violations and provide meaningful counterexamples. Finally, we summarize the lessons learned as well as the pending challenges for falsifiers to realize their potential on systems like OpenPilot.
@inproceedings{paperbot7201,
title = {Finding Property Violations through Network Falsification: Challenges, Adaptations and Lessons Learned from OpenPilot},
author = {Meriel von Stein and Sebastian Elbaum},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559500}
}