scholarly journals Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 177804-177814
Author(s):  
Jiangdong Liao ◽  
Teng Liu ◽  
Xiaolin Tang ◽  
Xingyu Mu ◽  
Bing Huang ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles

scholarly journals Heuristics-oriented overtaking decision making for autonomous vehicles using reinforcement learning

2020 ◽  
Vol 10 (4) ◽  
pp. 417-424
Author(s):  
Teng Liu ◽  
Bing Huang ◽  
Zejian Deng ◽  
Hong Wang ◽  
Xiaolin Tang ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles

Research on decision-making of autonomous vehicle following based on reinforcement learning method

2019 ◽  
Vol 46 (3) ◽  
pp. 444-452 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Kelong Wang ◽  
Guotao Xie ◽  
Yuchao Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning System ◽  
Explicit Calculation ◽  
Gradient Descent Method ◽  
Learning Method ◽  
Content Type ◽  
Car Following

Purpose Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.

Multi-Cameras based Decision Making at Mini-Roundabouts for Autonomous Vehicles

2019 ◽  
Author(s):  
Weichao Wang ◽  
Quang A Nguyen ◽  
Paul Wai Hing Chung ◽  
Qinggang Meng
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles
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