Open AccessA Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
Author(s) -
Yingfeng Cai,
Shaoqing Yang,
Hai Wang,
Chenglong Teng,
Long Chen
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3126796
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Following man-made rules in the traditional control method of autonomous driving causes limitations for intelligent vehicles under various traffic conditions that need to be overcome by incorporating machine learning-based method. The latter is inherently suitable for simple tasks of autonomous driving according to its limited characteristic under complex multi-lane traffic conditions. In this paper, a decision control method is proposed based on multi-task reinforcement learning to address the shortcomings of autonomous driving control under complex traffic conditions. Herein, the autonomous driving task is divided into several subtasks utilizing the proposed method to reduce the training time and improve traffic efficiency under complex multi-lane traffic condition. To ensure the efficiency and robustness of agent convergence to the optimal action space, an adaptive noise exploration method is designed for the subtasks with convex characteristics. Five-lane driving tasks scenarios embedded in Carla simulator have been conducted to verify the proposed method. The results of the simulation draw the conclusion that the proposed method increases the driving efficiency of intelligent vehicles under complex traffic conditions.