Coordinated adaptive cruise control with integration of driving behaviors based on prediction for surrounding vehicles status

Abstract

The adaptive cruise is an important vehicle control process for unmanned vehicles. This paper studies coordinated adaptive cruise control (ACC) of energy-saving in an intelligent networked environment, aiming to integrate control for common driving behaviors (cruising, following, lane-changing) by hierarchical control. The decision-making layer for integrating three driving behaviors is designed to improve the economy and passing time. Respectively, cruising strategy is optimized by considering efficient working area of motor; following strategy is constructed by dynamically adjusting the following distance with the state changes of the surrounding vehicles; lane-changing strategy is developed by considering the motor working efficiency of target speed. To improve the predictability of decision-making, non-linear autoregressive neural network (NARX) is introduced to predict the surrounding vehicle state. Furthermore, receiving the control reference from the decision layer, the controller is designed to realizes the tracking control of vehicle speed and following distance based on model predictive control (MPC), which is co-optimized for tracking, economy, and comfort. Eventually, the effectiveness of the coordinated ACC control is verified based on the hardware in the loop (HiL) test. The experiment results show that the vehicles with coordinated ACC can shorten the passing time and reduce the driving energy consumption to a certain extent, especially in the medium and low speed conditions.