Surrounding Vehicle Trajectory Prediction and Dynamic Speed Planning for Autonomous Vehicle in Cut-in Scenarios

Motion planning and vehicle prediction play an important role for autonomous vehicle, which aims to guarantee the driving safety under cut-in scenarios. This paper presents a hybrid prediction model for computing the future trajectory of the surrounding vehicles and a dynamic speed planner based on model predictive control to avoid collisions. Firstly, the hybrid prediction model combines the physics-based model and behavior-based model through Mamdani fuzzy logic. The predicted physic trajectory is computed using the constant yaw rate and velocity vehicle model. In addition, the prediction of driving intention is accomplished by using information of the difference between current motion and driving lanes. Furthermore, the predicted behavior trajectory is selected from the candidate quintic polynomial trajectory cluster through the designed cost function. Then, Gaussian propagation is applied at the fusion trajectory to compute the uncertainty distribution. Secondly, the dynamic speed planner based on model predictive control provides the optimal control command for the collision avoidance maneuver, which considers the future trajectory distribution of surrounding vehicles. Finally, the effectiveness of the proposed method is verified through simulation in different cut-in scenarios.