Improved Quintic Polynomial Autonomous Vehicle Lane-Change Trajectory Planning Based on Hybrid Algorithm Optimization

A trajectory planning method is proposed to address the lane-changing problem in intelligent vehicles. The method is based on quintic polynomial improvement. The transit position is determined according to the position and state of motion of the vehicle and the obstacle vehicle; the lane-changing process is divided into two segments. The quintic polynomials are commonly applied in trajectory planning, respectively, in the two segments. According to the different characteristics of the lane-changing paths in the front and rear segments, a multi-objective optimization function with different weight coefficients is established. A safe and comfortable lane-changing trajectory is achieved through the improved particle swarm optimization algorithm. Real-time simulation tests of lane-changing method are conducted on the hardware-in-the-loop platform. The method can be used in different scenarios to plan safe and comfortable trajectories.