Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making. (5th December 2018)

• Record Type: Journal Article
• Authors: Gao, Hongbo; Shi, Guanya; Xie, Guotao; Cheng, Bo
• Is Part Of: International journal of advanced robotic systems. Volume 15:Number 6(2018:Nov./Dec.)
• Publication Date: 2018-12-05
• Subjects: Car-following -- inverse reinforcement learning (IRL) -- autonomous vehicle -- decision-making -- automatic driving; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418817162
• Languages: English
• ISSNs: 1729-8806

Abstract

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment.