SMPC-Based Motion Planning of Automated Vehicle When Interacting With Occluded Pedestrians

Abstract

Driving in scenarios with occlusion is challenging but common in daily traffic, especially in urban and rural areas. To handle the potential interaction between the ego vehicle and pedestrian that possibly exists but is occluded by front vehicle, a stochastic model predictive control (SMPC)-based motion planning algorithm is proposed in this study. Firstly, a naturalistic driving dataset of vehicle-pedestrian interaction is established, based on which it is found that in the case of pedestrians passing or not, there are significant differences in front vehicle driving behavior. Then, a probability estimation approach for the presence of pedestrians in the occluded area is designed, which can achieve 91.9% accuracy in the naturalistic driving dataset. A phantom pedestrian model is established to quantify the uncertainty in the occluded area, which is further used to construct the chance constraint of the SMPC planning problem. Finally, a naturalistic driving data based simulation and a pedestrian-driver-in-the-loop experiment are carried out to validate the proposed algorithm. Both simulation and experiments show that our algorithm can effectively utilize the perceived information to speculate pedestrian presence beyond sensing range, thereby enabling proactive decisions to achieve safety, comfort and traffic efficiency in vehicle-pedestrian interactions. The proposed framework may find applications in interaction planning problems with uncertainty challenges.