Energy-efficient adaptive cruise control system with acceleration prediction via long short-term memory

Abstract

An energy-efficient adaptive cruise control (ACC) system that predicts the preceding state is proposed to meet the demands of driving in different states and working conditions. The long short-term memory (LSTM) network predicts the trajectory of the preceding vehicle’s future acceleration, allowing for an adaptive time headway that takes acceleration into account. The acceleration of the preceding vehicle is also incorporated as an augmented state in the deep deterministic policy gradient (DDPG) algorithm, resulting in a combined algorithm called prediction deep deterministic policy gradient (PDDPG). A multi-objective reward function is constructed based on human driving data to evaluate the performance indexes of vehicle longitudinal control, including efficiency, safety, and economy. The rules for changing the weight of each target performance under various typical cycle conditions are determined through experiments. The Carsim-based urban, suburban and highway conditions together with the Simulink vehicle dynamics model are compared with human driving data and ACC of conventional algorithms. Based on the test results, the proposed algorithm can improve fuel efficiency by 19.36% in urban driving conditions and reduce acceleration fluctuations.