Multi-Occlusions Inference from Multiple Social Vehicles

Occlusions at intersections threaten the safety of autonomous driving, especially in urban scenarios. Phantom vehicles are usually considered to mitigate the risks associated with the occlusions, and their states may be inferred from observable vehicles. This paper extends these methods to deal with multiple occlusions and multiple inferences. A multi-occlusion aware model allows for the concerns of occluded areas and the configuration of phantom vehicles. The evidence theory fuses multiple estimations to enhance occlusion inferences. Simulations for intersection scenarios are conducted to demonstrate the effectiveness of the proposed method for improving traffic efficiency.