Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving

TL;DR

This paper introduces a novel system for urban autonomous driving that estimates pedestrian emergence in occluded areas and assesses associated risks, enhancing safety and comfort in complex urban environments.

Contribution

It presents a new occlusion-aware risk assessment system that integrates pedestrian emergence probabilities into autonomous vehicle control strategies.

Findings

Outperforms baseline controllers in safety and comfort in simulated scenarios.

Effectively estimates pedestrian emergence probabilities in occluded regions.

Improves risk assessment accuracy for partially visible pedestrians.

Abstract

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.