Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving
This addresses a critical safety challenge for autonomous vehicles in urban environments, though it appears incremental as it builds on existing risk assessment concepts with a focus on occlusion handling.
The paper tackled the problem of avoiding unseen or partially occluded vulnerable road users in urban autonomous driving by proposing a system that estimates pedestrian emergence probabilities in occluded regions and incorporates them into a risk assessment framework and motion controller. The proposed controller outperformed baseline controllers in simulated tests with randomly placed parked cars and pedestrians, showing improvements in safety and comfort measures.
Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving. First, the proposed system utilizes available contextual information, such as visible cars and pedestrians, to estimate pedestrian emergence probabilities in occluded regions. These probabilities are then used in a risk assessment framework, and incorporated into a longitudinal motion controller. The proposed controller is tested against several baseline controllers that recapitulate some commonly observed driving styles. The simulated test scenarios include randomly placed parked cars and pedestrians, most of whom are occluded from the ego vehicle's view and emerges randomly. The proposed controller outperformed the baselines in terms of safety and comfort measures.