Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: \textit{how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment?} Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and time-consuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception systems. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3\% on challenging and 4\% on normal scenes. Our code will be released at https://github.com/XHwind/V2XP-ASG.

翻译：近期车联网通信技术的进步使自动驾驶车辆能够共享感知信息以获得更优的感知性能。随着自动驾驶车辆与智能基础设施的快速发展，车联网感知系统即将大规模部署，这引出了一个关键安全问题：\textit{在现实部署前，我们如何评估并提升其在复杂交通场景下的性能？}采集多样化的大规模真实道路测试场景看似最直接方案，但该方法成本高昂、耗时漫长，且只能覆盖有限场景。为此，我们提出首个开源对抗场景生成器V2XP-ASG，可为现代基于激光雷达的多智能体感知系统生成逼真且具有挑战性的场景。该生成器通过学习构建对抗协作图，以对抗且合理的方式同时扰动多个智能体的姿态。实验表明，V2XP-ASG能有效识别各类车联网感知系统的挑战性场景。同时，通过利用生成的有数量的挑战场景进行训练，车联网感知系统在挑战场景与常规场景下的精度分别提升12.3%和4%。我们的代码将发布于https://github.com/XHwind/V2XP-ASG。