Calibrating Perception Uncertainty for Autonomous Driving

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 favorable 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 a