Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving
This addresses safety challenges in automated driving for urban environments with limited visibility, though it appears incremental as it builds on existing optimization-based planning methods.
The paper tackles motion planning for automated driving under uncertain environments with occlusions by developing a method that ensures collision-free operation for worst-case scenarios, validated across multiple urban settings.
Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios.