一句话：提出首个面向V2X协同感知模块的自动化测试工具CooTest，通过特定蜕变关系与通信/天气变换算子检测错误行为。
自动驾驶需精确感知环境，V2X协同可弥补单车感知在远距离与遮挡上的不足，但通信挑战、DNN可解释性差及人工标注成本高使协同感知测试困难。本文提出CooTest，首个面向V2X协同感知模块的自动化测试工具。它设计V2X特定蜕变关系，提供模拟通信与天气影响的变换算子，并用V2X引导策略生成变换场景以提高测试效率。作者在多种融合方案的协同感知模型上评估，表明CooTest能有效检测各类V2X驾驶条件下的错误行为，且用生成场景重训练可提升检测平均精度并减少误导性协作错误。
Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) collaboration has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. However, despite spectacular progress, several communication challenges can undermine the effectiveness of multi-vehicle cooperative perception. The low interpretability of Deep Neural Networks (DNNs) and the high complexity of communication mechanisms make conventional testing techniques inapplicable for the cooperative perception of autonomous driving systems (ADS). Besides, the existing testing techniques, depending on manual data collection and labeling, become time-consuming and prohibitively expensive. In this paper, we design and implement CooTest, the first automated testing tool of the V2X-oriented cooperative perception module. CooTest devises the V2X-specific metamorphic relation and equips communication and weather transformation operators that can reflect the impact of the various cooperative driving factors to produce transformed scenes. Furthermore, we adopt a V2X-oriented guidance strategy for the transformed scene generation process and improve testing efficiency. We experiment CooTest with multiple cooperative perception models with different fusion schemes to evaluate its performance on different tasks. The experiment results show that CooTest can effectively detect erroneous behaviors under various V2X-oriented driving conditions. Also, the results confirm that CooTest can improve detection average precision and decrease misleading cooperation errors by retraining with the generated scenes.
@inproceedings{paperbot8446,
title = {CooTest: An Automated Testing Approach for V2X Communication Systems},
author = {An Guo and Xinyu Gao and Zhenyu Chen and Yuan Xiao and Jiakai Liu and Xiuting Ge and Weisong Sun and Chunrong Fang},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3680373}
}