A real-time lane change trajectory planning approach for autonomous vehicles utilizing tire force prediction

For lane change behavior under extreme operating conditions, existing models cannot calculate in real time the tire force of the vehicle lane change over a sufficiently long time frame in the future. In order to address this problem, a novel scheme is presented for real-time trajectory planning of autonomous vehicles, which incorporates personalized vehicle dynamics. We first establish lateral dynamics models for four-wheel-steering and front-wheel-steering vehicles along with a nonlinear tire model. Then, we construct a fuzzy logic mechanism to characterize the relationship between the vehicle lateral/longitudinal acceleration and the future lateral/longitudinal tire force, to quantify whether the vehicle tire force reaches saturation in trajectory planning in real time. A safety assessment model is introduced to measure the risk of side slippage of the vehicle and collision under extreme operating conditions. In addition, lane change behavior is designed as a nonlinear programming model and a gradient descent method is used to obtain optimal lateral and longitudinal accelerations online. The geometric curve fitting method is utilized to generate the lane change trajectory. The simulation results using MATLAB/Simulink demonstrate that the solution time of our method is significantly lower than that of the widely used vehicle dynamics method and the newest Neural Network method, which can realize real-time prediction of the maximum tire force before lane change. Moreover, our method improves the ability to calculate the risk of longitudinal and lateral coupling of a lane change in extreme operating conditions and then realizes trajectory planning in a vehicle-dynamics-specific way.