一句话：结合程序化内容生成与基于搜索的测试，自动为自动驾驶车道保持软件生成能暴露失效的虚拟道路场景。
在真实交通中测试自动驾驶汽车软件既昂贵又危险，甚至已造成人员伤亡；在仿真中进行虚拟测试是更高效、更安全的替代方案，但构建合适的测试场景本身非常费力且困难。本文提出将现代电子游戏中常用的程序化内容生成（procedural content generation）与在多个领域已被证明有效的基于搜索的测试（search-based testing）相结合，来自动创建具有挑战性的虚拟场景，用于测试自动驾驶汽车软件。作者实现了原型工具 AsFault，针对自动驾驶的标志性功能之一——车道保持（lane keeping）生成虚拟道路网络。评估在两个不同的自动驾驶汽车软件系统上进行，结果表明 AsFault 能够生成有效的虚拟道路网络，成功暴露软件失效，失效表现为车辆偏离车道；与随机测试相比，AsFault 不仅更高效，而且最多能造成两倍的车道偏离。
Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car soft- ware. Our AsFault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that AsFault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing AsFault was not only more efficient, but also caused up to twice as many lane departures.
DOI 原文 ·
@inproceedings{paperbot5378,
title = {Automatically testing self-driving cars with search-based procedural content generation},
author = {Alessio Gambi and Marc Mueller and Gordon Fraser},
booktitle = {ISSTA},
year = {2019},
doi = {10.1145/3293882.3330566}
}