  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.
