Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird's Eye View(BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmentation has emerged as a prominent approach in autonomous driving. However, the current approaches have two critical gaps. First, the optimization process is simplistic and involves just evaluating a fixed set of trajectories over the cost map. The trajectory samples are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty in the cost maps that can arise due to noise in RGB images, and BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, merging, etc from the neighboring vehicles. In this paper, we propose UAP-BEV: A novel approach that models the noise in Spatio-Temporal BEV predictions to create an uncertainty-aware occupancy grid map. Using queries of the distance to the closest occupied cell, we obtain a sample estimate of the collision probability of the ego-vehicle. Subsequently, our approach uses gradient-free sampling-based optimization to compute low-cost trajectories over the cost map. Importantly, the sampling distribution is adapted based on the optimal cost values of the sampled trajectories. By explicitly modeling probabilistic collision avoidance in the BEV space, our approach is able to outperform the cost-map-based baselines in collision avoidance, route completion, time to completion, and smoothness. To further validate our method, we also show results on the real-world dataset NuScenes, where we report improvements in collision avoidance and smoothness.

翻译：自动驾驶需要从原始传感器数据中准确推理物体的位置。最近的端到端学习方法通过鸟瞰视图分割作为可解释的中间表示，将原始传感器数据直接映射到轨迹输出。基于鸟瞰视图分割生成代价地图的运动规划已成为自动驾驶领域的主流方法。然而，现有方法存在两个关键缺陷:首先，优化过程过于简化，仅对固定轨迹集进行代价地图评估，且轨迹样本未根据其对应的代价值进行自适应调整；其次，现有代价地图未考虑因RGB图像噪声和鸟瞰图标注不确定性可能导致的代价地图不确定性。因此，当面临相邻车辆的突然切入、停车、超车、并线等挑战性场景时，这些方法可能表现不佳。本文提出UAP-BEV:一种新颖方法，通过建模时空鸟瞰图预测中的噪声来构建不确定性感知占据栅格地图。基于到最近占据单元距离的查询，我们获得自车碰撞概率的样本估计值。随后，采用无梯度采样子优化方法在代价地图上计算低代价轨迹。重要的是，采样分布会根据采样轨迹的最优代价值进行自适应调整。通过在鸟瞰视图空间中显式建模概率碰撞规避，本方法在碰撞规避、路径完成率、完成时间和平滑度方面均优于基于代价地图的基线方法。为验证方法有效性，我们还在真实世界数据集NuScenes上展示结果，表明在碰撞规避和平滑度方面均有改进。