Collision Preventive Velocity Planning based on Static Environment Representation for Autonomous Driving in Occluded Region

This paper presents the collision preventive velocity planning algorithm to enhance safety when driving the occluded region in an urban environment. The accidents due to the pedestrian who appear from the occluded region occur frequently on complex urban roads. The collision preventive velocity planner has been proposed to reduce the potential risk caused by the occluded region of sensors. The point cloud of 2D Lidar is processed to construct the static obstacle map. A static obstacle boundary is defined to estimate the possible position of the pedestrian appearance based on the static obstacle map. The longitudinal motion planner determines the target states using the static obstacle boundary to prevent the inevitable collision with pedestrians coming from the occluded region. The target states are tracked by MPC based motion tracker to determine the desired acceleration. The collision prevention performance of the proposed algorithm has been validated by the Monte-Carlo simulation. The simulation results demonstrated that the proposed algorithm prevents the collision with pedestrian from the occluded region and improve the safety of urban autonomous driving.