Citation

Fu Y, Li C, Yu FR, Luan TH, Zhang Y. IEEE Trans. Vehicular Tech. 2020; 69(6): 5876-5888.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TVT.2020.2986005

PMID

unavailable

Abstract

Autonomous braking through vehicle precise decision-making and control to reduce accidents is a key issue, especially in the early diffusion phase of autonomous vehicle development. This paper proposes a deep reinforcement learning (DRL)-based autonomous braking decision-making strategy in an emergency situation. Three key influencing factors, including efficiency, accuracy and passengers' comfort, are fully considered and satisfied by the proposed strategy. First, the vehicle lane-changing process and the braking process are analyzed in detail, which include the critical factors in the design of the autonomous braking strategy. Second, we propose a DRL process that determines the optimal strategy for autonomous braking. Particularly, a multi-objective reward function is designed, which can compromise the rewards achieved of different brake moments, the degree of the accident, and the comfort of the passenger. Third, a typical actor-critic (AC) algorithm named deep deterministic policy gradient (DDPG) is adopted for solving the autonomous braking problem, which can improve the efficiency of the optimal strategy and be stable in continuous control tasks. Once the strategy is well trained, the vehicle can automatically take optimal braking behavior in an emergency to improve driving safety. Extensive simulations validate the effectiveness and efficiency of our proposal in terms of learning effectiveness, decision-making accuracy and driving safety.

Language: en

Keywords

Accidents; Actor-Critic; actor-critic problem; autonomous braking; autonomous vehicle development; Autonomous vehicles; brake moments; brakes; braking; connected and autonomous vehicle; decision making; Decision making; decision-making accuracy; deep deterministic policy gradient; deep reinforcement learning; Deep reinforcement learning; driving safety; DRL process; emergency situation; gradient methods; learning (artificial intelligence); Learning (artificial intelligence); Machine learning; mobile robots; multiobjective reward function; neural nets; optimal braking behavior; optimal strategy; optimisation; passengers comfort; road accidents; road safety; road traffic control; road vehicles; Safety; vehicle autonomous braking; vehicle lane-changing process; vehicle precise decision-making