Motion Planning Algorithm for Autonomous Driving Based on Trajectory Prediction

To solve the problems of trajectory prediction and obstacle avoidance of self-vehicles in autonomous driving, a obstacle avoidance algorithm that combines trajectory prediction and vehicle motion planning is proposed. Firstly, in this paper, Unscented Kalman filter and constant acceleration model, namely UKF + CA, as well as Hidden Markov model, namely HMM, are combined together. Predict the trajectory of the vehicle in front and integrate the prediction results obtained by these two methods, which can improve the accuracy of the prediction. Then, in the Frenet coordinate system, this paper adopts the methods of dynamic programming and quadratic programming to generate the planning trajectory of the self-aircraft. After that, this paper conducts collision detection between the fusion trajectory of the preceding vehicle and the planning trajectory of the self-vehicle. If there is a risk of collision, a virtual obstacle will be generated and the path will be re-planned to avoid the obstacle. The simulation results show that, whether in the straight-going scenario or the lane-changing scenario, this method can effectively improve the accuracy of predicting the trajectory of the vehicle in front, effectively avoid dynamic obstacles, enhance the driving efficiency, and also improve the safety and comfort of driving.