The reliability of current autonomous driving systems is often jeopardized in situations when the vehicle's field-of-view is limited by nearby occluding objects. To mitigate this problem, vehicle-to-vehicle communication to share sensor information among multiple autonomous driving vehicles has been proposed. However, to enable timely processing and use of shared sensor data, it is necessary to constrain communication bandwidth, and prior work has done so by restricting the number of other cooperative vehicles and randomly selecting the subset of vehicles to exchange information with from all those that are within communication range. Although simple and cost effective from a communication perspective, this selection approach suffers from its susceptibility to missing those vehicles that possess the perception information most critical to navigation planning. Inspired by recent multi-agent path finding research, we propose a novel selective communication algorithm for cooperative perception to address this shortcoming. Implemented with a lightweight perception network and a previously developed control network, our algorithm is shown to produce higher success rates than a random selection approach on previously studied safety-critical driving scenario simulations, with minimal additional communication overhead.

翻译：当前自动驾驶系统在车辆视野受周围遮挡物限制时，其可靠性往往受到威胁。为解决该问题，学界提出了通过车-车通信实现多辆自动驾驶车辆间传感器信息共享的方案。然而，为实现共享传感器数据的实时处理与应用，必须限制通信带宽——现有研究通过限制协同车辆数量，并从通信范围内所有车辆中随机选取信息交换子集来实现带宽约束。尽管从通信角度看简单且经济，但这种选取方法极易遗漏那些掌握导航规划最关键的感知信息的车辆。受近期多智能体路径规划研究的启发，我们提出一种面向协同感知的新型选择性通信算法以弥补上述缺陷。通过轻量级感知网络与已有控制网络的联合实现，实验表明：在先前研究的安全关键型驾驶场景模拟中，该算法相比随机选取方法能获得更高的任务成功率，且仅增加极少的通信开销。