Corridor Selection Under Semantic Uncertainty for Autonomous Road Vehicles

Automated driving systems are likely to encounter situations in which their understanding of the road is uncertain. State-of-the-art systems only act on a single road hypothesis. If that hypothesis is incorrect, the likely consequence is an undesired behavior of the automated vehicle. This paper shows that the consideration of multiple road hypotheses in the selection of a driving corridor can improve the performance of an automated driving system and relax the requirements on its perception system. Two algorithms are presented that infer a corridor from a multi-hypothesis road representation with respect to different navigational goals.