Uncertainty-Aware Planning in Bird’s Eye View Representations

Abstract

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, 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, which are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty arising from noise in RGB images, BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, and merging from neighboring vehicles. In this thesis, we propose UAP-BEV, a novel approach that models the noise in Spatio-Temporal BEV predictions to create uncertainty-aware occupancy grid maps. 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. Notably, 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 can 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 collision avoidance and smoothness improvements. It also outperforms other uncertainty-based methods in conservatism.