Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting the motion of surrounding agents and planning its own motion safely. Doing so is challenging in complex urban environments due to frequent occlusions and interactions among many agents. One solution is to leverage smart infrastructure to augment a CAV's situational awareness; the present work leverages a recently proposed "Self-Supervised Traffic Advisor" (SSTA) framework of smart sensors that teach themselves to generate and broadcast useful video predictions of road users. In this work, SSTA predictions are modified to predict future occupancy instead of raw video, which reduces the data footprint of broadcast predictions. The resulting predictions are used within a planning framework, demonstrating that this design can effectively aid CAV motion planning. A variety of numerical experiments study the key factors that make SSTA outputs useful for practical CAV planning in crowded urban environments.

翻译：网联自动驾驶车辆有望提升城市交通的安全性、效率和可持续性。然而，这依赖于网联自动驾驶车辆正确预测周围交通参与者的运动并安全规划自身运动的能力。在复杂的城市环境中，由于频繁的遮挡和多交通参与者之间的交互，这一任务极具挑战性。一种解决方案是利用智能基础设施增强网联自动驾驶车辆的情境感知能力；本研究采用了近期提出的"自监督交通顾问"框架，该框架中的智能传感器能够自主学习生成并广播道路使用者的有用视频预测。在本工作中，自监督交通顾问的预测被修改为预测未来占用情况而非原始视频，从而减少了广播预测的数据占用。所得预测结果被集成到规划框架中，验证了该设计可有效辅助网联自动驾驶车辆运动规划。通过一系列数值实验，研究了使自监督交通顾问输出在拥挤城市环境中对实际网联自动驾驶车辆规划具有实用价值的关键因素。