Abstract
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.
Abstract (translated)
联网自主车辆(CAV)承诺提高城市交通的安全性、效率和可持续性。然而,这需要CAV准确预测周围车辆的移动并安全地规划自己的移动。在复杂的城市环境中,由于许多车辆的频繁阻挡和交互,这样做很困难。一种解决方案是利用智能基础设施来提高CAV的环境意识;目前的研究利用最近提出的智能传感器提出的“自主交通顾问”(SSTA)框架,这些传感器自我学习生成和广播道路使用者有用的视频预测。在本研究中,SSTA的预测被修改,以预测未来的使用情况,而不是原始视频,从而减少了广播预测的数据足迹。结果的预测被用于规划框架中,这表明这种方法可以有效地帮助CAV移动规划,在拥挤的城市环境中提高CAV的使用效率。多种数值实验研究了使SSTA输出在拥挤城市环境中对CAV规划有用的关键因素。
URL
https://arxiv.org/abs/2309.07504
