Adaptive Cruise Control Algorithm Based on Multi-Vehicle Information and Velocity Disturbance Compensation

Aiming at the problem in the adaptive cruise control (ACC) system only the single-target vehicle state information is used as the basis for decision-making which affects the actual control stability. This paper proposes a multi-vehicle environmental potential field spacing strategy, suggests the desired spacing decision, and proposes a combination of extended state observer and model predictive control (MPC) algorithm. As a multi-vehicle potential field spacing strategy established for desired cruise distance calculation, the compensation for possible prediction model mismatches and sensor noise in the MPC part in real time improves the quality of vehicle spacing and speed control. Finally, based on the Simulink-Carsim platform, simulation-in-loop scenarios were verified. The results show that the vehicle speed overshoot is reduced after the compensation algorithm is adopted, and the distance control accuracy is improved at 0.67m in the steady-state car-following state, so the safety and longitudinal stability of the ACC control are improved.