Research on Intelligent Vehicle Motion Planning Based on Pedestrian Future Trajectories

Abstract

This work proposes an improved pedestrian social force model for pedestrian trajectory prediction to prevent intelligent vehicles from colliding with pedestrians while driving on the road. In this model, the intelligent vehicle performs motion planning on the basis of predicted pedestrian trajectory results. A path is planned by using the fifth-order Bezier curve, the optimal coordinate is acquired by adjusting the weight coefficient of each optimisation goal, and the optimal driving trajectory curve is planned. In speed planning, the pedestrian collision boundary is proposed to ensure pedestrian safety. The initial speed planning is performed by a dynamic programming algorithm, and then the optimal speed curve is obtained by quadratic programming. Finally, the front pedestrian deceleration or uniform speed scene is set for simulation verification. Simulation results show that the vehicle speed reaches a maximum value of 6.39 m/s under the premise of ensuring safety and that the average speed of the intelligent vehicle is 4.6 m/s after a normal start process. The maximum and average speed values obtained with trajectory prediction indicate that the intelligent vehicle ensures pedestrian and vehicle safety as well as the intelligent vehicle’s economy.