Joint Optimization of Sensing, Decision-Making and Motion-Controlling for Autonomous Vehicles: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Joint Optimization of Sensing, Decision-Making and Motion-Controlling for Autonomous Vehicles: A Deep Reinforcement Learning Approach

Published:2022-05 Issue:5 Volume:71 Page:4642-4654
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Chen Longquan¹^ORCID,He Ying¹^ORCID,Wang Qiang²,Pan Weike¹^ORCID,Ming Zhong¹^ORCID

Affiliation:

1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

2. National Key Laboratory of Science and Technology on Information System Security, Institute of System Engineering, Chinese Academy of Military Science, Beijing, China

Funder

National Natural Science Foundation of China

Foundation for Distinguished Young Talents in Higher Education of Guangdong

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/9779385/09712351.pdf?arnumber=9712351

Reference44 articles.

1. Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving

2. A Reinforcement Learning Approach to Autonomous Decision Making of Intelligent Vehicles on Highways

3. Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

4. Congested traffic states in empirical observations and microscopic simulations

5. Asynchronous methods for deep reinforcement learning;mnih;Proc Int Conf Mach Learn,0

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