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.

本文提出了一种基于选择性通信算法的合作感知方法，用于提高当前自动驾驶系统在障碍物干扰场景下的可靠性。通过多智能体路径规划研究的启发，将感知网络与控制网络相结合，实现了对有关导航规划最为关键的感知信息的选择传输，在安全关键驾驶场景模拟中显示出了比随机选择方法更高的成功率，同时最小化了附加通信开销。