Calibrating Perception Uncertainty for Autonomous Driving

Abstract

Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception models in these systems perform well in detecting objects under favourable conditions but their performance deteriorates in poor visibility or with partly occluded objects. To reduce risks from undetected objects, autonomous vehicles must incorporate all relevant uncertainties into their decision-making processes. Grid-based perception outputs, such as occupancy grids, and object-based outputs, like lists of detected objects, must be accompanied by well-calibrated uncertainty estimates. These uncertainties are essential for ensuring the model's reliability and safety. In this paper, we identify limitations in the current state-of-the-art and propose a more comprehensive set of uncertainty estimates that should be reported. In addition to commonly estimated forms of uncertainty about the presence, location, shape, and trajectory of detected objects, we propose to quantify the uncertainty about undetected objects within a region. Access to this set of uncertainties enables planners to perform region occupancy queries, which provide the probability that a certain region, such as the area around a chosen trajectory, is free of obstacles. We propose a novel approach for generating these probabilistic outputs from bird's-eye-view (BEV) probabilistic semantic segmentation. Our experiments demonstrate that the initial probabilistic outputs from segmentation are not calibrated, and we present methods to achieve well-calibrated uncertainty estimates. Finally, we conduct an experiment on a downstream task, underscoring the importance of calibrated uncertainties for planning and highlighting the advantages of including additional uncertainty types.