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Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

Gao, Hongbo and Shi, Guanya and Xie, Guotao and Cheng, Bo (2018) Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making. International Journal of Advanced Robotic Systems, 15 (6). pp. 1-11. ISSN 1729-8814. http://resolver.caltech.edu/CaltechAUTHORS:20190102-155140213

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1177/1729881418817162DOIArticle
ORCID:
AuthorORCID
Gao, Hongbo0000-0002-5271-1280
Additional Information:© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Received: May 09, 2018; Accepted: October 11, 2018. Article first published online: December 6, 2018; Issue published: November 1, 2018. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Junior Fellowships for Advanced Innovation Think-tank Program of China Association for Science and Technology under grant no. DXB-ZKQN-2017-035, Project funded by China Postdoctoral Science Foundation under grant no. 2017M620765, Project funded by China Postdoctoral Science Foundation Special Foundation under grant no. 2018T110095, the National Key Research and Development Program of China under grant no. 2017YFB0102603. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funders:
Funding AgencyGrant Number
China Association for Science and TechnologyDXB-ZKQN-2017-035
China Postdoctoral Science Foundation2017M620765
China Postdoctoral Science Foundation2018T110095
National Key Basic Research Program of China2017YFB0102603
Subject Keywords:Car-following, inverse reinforcement learning (IRL), autonomous vehicle, decision-making, automatic driving
Record Number:CaltechAUTHORS:20190102-155140213
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190102-155140213
Official Citation:Gao, Hongbo, et al. “Car-Following Method Based on Inverse Reinforcement Learning for Autonomous Vehicle Decision-Making.” International Journal of Advanced Robotic Systems, Nov. 2018, doi:10.1177/1729881418817162.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92021
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:03 Jan 2019 19:23
Last Modified:03 Jan 2019 19:23

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