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:本文涉及多试剂联合感知和规划的任务, 特别是因为它涉及到连接自驾驶车辆的无碰撞导航的现实世界挑战。 对于这项任务, 几个具有通信功能的车辆必须穿越繁忙的交叉路口, 避免彼此相撞和遇到障碍。 为此, 本文提出一个基于成本图的可学习规划机制, 考虑到原始的感知数据, 即(1) 分布的, (2) 不确定性意识和(3) 带宽效率。 我们的方法产生了一个成本图和具有不确定性的诱导图, 以便根据对多个试剂的评估, 分解和激活候选轨迹。 提议的方法显示了一套开放源间接费用数据集以及新颖的通信关键模拟器的若干有利性性性性能趋势。 它产生精确的静态占用预测, 作为一种中间感知输出, 达到平均72.5%的像素偏误差精度。 通过选择顶轨, 多试剂的尺度以及制剂的数量, 将硬碰撞率降低到57%, 与8个试剂相比, 。</s>