Near-optimal solutions to lane change path planning for an intelligent vehicle in presence of moving obstacles

Based on polynomial theory and radial basis function (RBF) neural network, a path planning method for the intelligent vehicles lane changing process was proposed. The near-optimal solutions of the lane changing path in the fixed boundary conditions can be obtained by this method. In this method the lane changing vehicle and obstacle vehicles were presented by rectangle, and then in the constraints of collision detect conditions, boundary conditions and comfort performance index that the near-optimal solutions of the lane changing path were calculated. In addition, the dynamic RBF neural network was used to solve the problem that how to select a reasonable boundary conditions. By this dynamic RBF neural network the reasonable boundary conditions were calculated and the neural network has the function of online learning, which was optimized by itself. Simulation results prove the correctness and feasibility of this algorithm, and illustrative examples show the advantage of this new method in the case of lane changing with multiple obstacles.