一句话：提出 LawBreaker，用驾驶员导向的法规规约语言和覆盖最大化模糊测试来发现自动驾驶系统的交通法规违反场景。
自动驾驶系统在部署前需要充分测试，高保真模拟器可提供多样场景，包括现实中难以复现的情形。既有测试生成方法多采用弱预言，如到达终点且无碰撞，却未评估行程本身是否安全合法。本文提出 LawBreaker，一个用于依据真实交通法规测试 ADS 的自动化框架，设计上兼容不同场景描述语言。它提供面向驾驶员的交通法规规约语言，并包含模糊测试引擎，通过最大化规约覆盖来搜索不同的违规方式。作者在 Apollo+LGSVL 上实现该框架并规约中国交通法规。评估发现 14 项法规违反，包括 173 个导致事故的测试用例。
Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose , an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario description languages. provides a rich driver-oriented specification language for describing traffic laws, and a fuzzing engine that searches for different ways of violating them by maximising specification coverage. To evaluate our approach, we implemented it for Apollo+LGSVL and specified the traffic laws of China. was able to find 14 violations of these laws, including 173 test cases that caused accidents.
@inproceedings{paperbot7370,
title = {LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles},
author = {Yang Sun and Christopher M. Poskitt and Jun Sun and Yuqi Chen and Zijiang Yang},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556897}
}