In this work, we consider the task of collision-free trajectory planning for connected self-driving vehicles. We specifically consider communication-critical situations--situations where single-agent systems have blindspots that require multi-agent collaboration. To identify such situations, we propose a method which (1) simulates multi-agent perspectives from real self-driving datasets, (2) finds scenarios that are challenging for isolated agents, and (3) augments scenarios with adversarial obstructions. To overcome these challenges, we propose to extend costmap-based trajectory evaluation to a distributed multi-agent setting. We demonstrate that our bandwidth-efficient, uncertainty-aware method reduces collision rates by up to 62.5% compared to single agent baselines.

翻译：在这项工作中，我们考虑了联网自动驾驶车辆的无碰撞轨迹规划任务。我们特别关注通信关键场景——即单一智体系统存在盲点、需要多智体协作的场景。为识别此类场景，我们提出了一种方法，该方法：（1）基于真实自动驾驶数据集模拟多智体视角，（2）发现孤立智体难以应对的场景，以及（3）通过对抗性障碍增强场景。为克服这些挑战，我们提出将基于代价地图的轨迹评估扩展到分布式多智体场景中。我们证明了这种带宽高效且考虑不确定性的方法，相比单一智体基线，可将碰撞率降低高达62.5%。