An Adaptive Cruise Control Method Based on Improved Variable Time Headway Strategy and Particle Swarm Optimization Algorithm

During the car following process, in order to improve ACC ability to coordinate various goals and adaptability in a complex and variable driving environment, the variable time headway spacing strategy and multi-objective adaptive cruise control algorithm are studied. Aiming at the deceleration adaptability, an improved variable time headway strategy is proposed, which adjusts the value of acceleration weight according to the deceleration duration and deceleration changes to increase the relative distance between the two vehicles in deceleration conditions. In order to improve the multi-objective coordination ability, the longitudinal kinematics model between the two vehicles is established, the objective function and constraints considering multiple factors are designed, and the relaxation factor vector is introduced to soften the boundary of different hard constraints to solve the problem with no feasible solution. Based on model predictive control theory, the objective function is converted into a multi-constrained quadratic programming problem in the rolling optimization process, and an improved particle swarm algorithm is used to solve the function. Through numerical simulation, the results show that the improved ACC algorithm can effectively improve the safety of the vehicle during deceleration, as well as the fuel economy, comfort and tracking ability under cycling conditions. The effectiveness of the algorithm is verified by the joint simulation of MATLAB/Simulink and CarSim.