An Adaptive Cruise Control Strategy with Improved Preceding Vehicle State Prediction

Abstract:

To address the issue of model mismatch and performance degradation caused by uncertainties in the motion state of a preceding vehicle within adaptive cruise control （ACC） systems， a strategy for ACC by improving the prediction of the state of the preceding vehicle was proposed. Initially， the future acceleration trajectory of the preceding vehicle is predicted using a time convolutional network （TCN）， which makes use of historical speed and acceleration information. Subsequently， the acceleration predicted is employed as a disturbance to formulate the predictive control model for the ACC system. Finally， simulation experiments are conducted on the Matlab-Carsim joint simulation platform. The experimental results demonstrate that favorable predictive results for vehicle acceleration are achieved by the TCN. Furthermore， compared to the traditional model predictive control （MPC）， the improved method leads to the reduction in velocity tracking errors and the enhancement in responsive speed of the following vehicle towards changes in the speed of the preceding vehicle.

Key words:adaptive cruise control (ACC) system;model predictive control (MPC);time convolutional network (TCN);acceleration trajectory prediction