Investigation of a longitudinal and lateral lane-changing motion planning model for intelligent vehicles in dynamical driving environments

Abstract

This paper describes a lane-changing motion planning model for intelligent vehicles under constraints of collision avoidance in dynamical driving environments. The key innovation is decoupling the longitudinal and lateral motion planning to realize trajectory re-planning in a normal lane-changing process to prevent collisions. The longitudinal planning model decides a collision-free termination point of motion through planning the longitudinal acceleration and velocity. Taking the termination time as input, the lateral planning model plans the optimal reference trajectory for normal lane-changing maneuvers or re-plans the lane-changing trajectory to eliminate potential accidents. When traffic states have variations that may bring about the collisions, the termination point can be updated through the longitudinal planning model, based on which the lateral planning model makes adjustments to the pre-planned trajectory to complete the lane-changing successfully or return its original lane. The simulation results show that the proposed model not only can handle the general motion planning problem but also can re-plan trajectories in emergent conditions to ensure safety, while vehicle dynamics retain in a stable state during the lane-changing or returning maneuvers.