一句话：提出 ATLAS，按地图拓扑对交叉口车道分类，用遗传算法生成碰撞避免测试场景，实现全局地图系统化测试。
自动驾驶系统依赖地图，全局地图测试可保证不同场景下车辆安全，但复杂车道连接导致场景数量爆炸。本文提出 ATLAS，一种基于地图拓扑场景分类的 ADS 碰撞避免测试方法。其核心是按交叉口车道的拓扑交互模式进行分类，使同类车道触发相似的避碰机动；每类仅选一条代表车道构建测试场景，并用遗传算法生成测试用例。作者在百度 Apollo 6.0、LGSVL 模拟器和旧金山地图上实现并评估 ATLAS，发现九类真实问题，并将需测试的交叉口车道数减少 98%。
Autonomous Driving Systems (ADSs), which replace humans to drive vehicles, are complex software systems deployed in autonomous vehicles (AVs). Since the execution of ADSs highly relies on maps, it is essential to perform global map-based testing for ADSs to guarantee their correctness and AVs’ safety in different situations. Existing methods focus more on specific scenarios rather than global testing throughout the map. Testing on a global map is challenging since the complex lane connections in a map can generate enormous scenarios. In this work, we propose ATLAS, an approach to ADSs’ collision avoidance testing using map topology-based scenario classification. The core insight of ATLAS is to generate diverse testing scenarios by classifying junction lanes according to their topology-based interaction patterns. First, ATLAS divides the junction lanes into different classes such that an ADS can execute similar collision avoidance maneuvers on the lanes in the same class. Second, for each class, ATLAS selects one junction lane to construct the testing scenario and generate test cases using a genetic algorithm. Finally, we implement and evaluate ATLAS on Baidu Apollo with the LGSVL simulator on the San Francisco map. Results show that ATLAS exposes nine types of real issues in Apollo 6.0 and reduces the number of junction lanes for testing by 98%.
DOI 原文 ·
@inproceedings{paperbot6593,
title = {Systematic Testing of Autonomous Driving Systems Using Map Topology-Based Scenario Classification},
author = {Yun Tang and Yuan Zhou and Tianwei Zhang and Fenghua Wu and Yang Liu and Gang Wang},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678735}
}