Prediction Based Trajectory Planning for Safe Interactions Between Autonomous Vehicles and Moving Pedestrians in Shared Spaces

It is an essential issue for autonomous vehicles to keep driving safety while sharing spaces with other road users, especially moving pedestrians. An ideal trajectory planning of autonomous vehicles should be collision-free and feasible for the vehicles to execute. Previous efforts on trajectory planning mainly focused on traditional roads, and the research into pedestrian-vehicle interaction in shared spaces were still insufficient. This study proposed a prediction-planning collaboration method for autonomous vehicles to avoid collisions with moving pedestrians by predicting their motions, and generate feasible trajectories based on Frenet coordinate in shared spaces. Pre-crash scenarios were designed based on reported crashes, and simulations were performed to evaluate the collision avoidance performances of the proposed method. Meanwhile, validations with real world dataset were conducted to verify the practicability of the proposed framework in real driving environment. The results indicated that the proposed prediction-planning collaboration method could effectively predict the motions of moving pedestrians and ensure the safe interactions between autonomous vehicles and pedestrians in shared spaces.