Predictive Motion Planning of Vehicles at Intersection Using a New GPR and RRT

This paper addresses the challenge of safe path planning for autonomous vehicles at intersections. Rapidly exploring Random Tree (RRT) as an effective local motion planning methodology has the ability to determine a feasible path. As the number of sampled positions increases, the probability of finding an optimal path increases. However, RRT is usually applied to the static environment due to its delay or lack of efficiency in planning a path to the goal area. In dynamic environments, redundant sampling positions near dynamic obstacles are not effective. Therefore, we proposed a methodology, pRRT, that combines Gaussian Processes Regression (GPR) and RRT to generate a local path to guide the vehicle through the intersection. The procedure includes two phases: prediction and planning. Under prediction, GPR predicts the vehicle's future trajectory points. The prediction results are combined with destination position (intersection exit) to generate a probability map for sampling such that position sample quality is increased by avoiding redundant samples. The optimal strategy is deployed to guarantee the trajectory is collision-free in both current and future time instances. A combination of both proposed improvements can thus result in a path that is collision-free under the dynamic intersection area. The proposed method also increased the speed of path generation compared to the RRT algorithm.