This paper addresses the task of joint multi-agent perception and planning, especially as it relates to the real-world challenge of collision-free navigation for connected self-driving vehicles. For this task, several communication-enabled vehicles must navigate through a busy intersection while avoiding collisions with each other and with obstacles. To this end, this paper proposes a learnable costmap-based planning mechanism, given raw perceptual data, that is (1) distributed, (2) uncertainty-aware, and (3) bandwidth-efficient. Our method produces a costmap and uncertainty-aware entropy map to sort and fuse candidate trajectories as evaluated across multiple-agents. The proposed method demonstrates several favorable performance trends on a suite of open-source overhead datasets as well as within a novel communication-critical simulator. It produces accurate semantic occupancy forecasts as an intermediate perception output, attaining a 72.5% average pixel-wise classification accuracy. By selecting the top trajectory, the multi-agent method scales well with the number of agents, reducing the hard collision rate by up to 57% with eight agents compared to the single-agent version.

翻译：本文研究了联合多智能体感知与规划任务，尤其关注联网自动驾驶车辆在真实场景中的无碰撞导航挑战。在该任务中，多个具备通信能力的车辆需在繁忙交叉路口协同行驶，同时避免相互碰撞及与障碍物碰撞。为此，本文提出一种基于可学习代价地图的规划机制，该机制以原始感知数据为输入，具备以下特性：(1)分布式，(2)不确定性感知，(3)带宽高效。该方法生成代价图与不确定性感知熵图，用于多智能体间候选轨迹的排序与融合。实验表明，所提方法在一组开源俯视数据集及新型通信关键型模拟器上展现出多项优越性能趋势：作为中间感知输出，该方法可生成精确的语义占据预测结果，达到72.5%的平均像素级分类准确率。通过选择最优轨迹，该多智能体方法随智能体数量增加表现可扩展性，与单智能体版本相比，在八智能体场景下硬碰撞率降低高达57%。