NDLI: Deep reinforcement-learning-based driving policy for autonomous road vehicles

Content Provider	IET Digital Library
Author	Makantasis, Konstantinos Kontorinaki, Maria Nikolos, Ioannis
Abstract	In this work, the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of the authors’ knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles’ behaviour on the overall traffic flow are presented.
Starting Page	13
Ending Page	24
Page Count	12
ISSN	1751956X
Volume Number	14
e-ISSN	17519578
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0249
Journal	IET Intelligent Transport Systems
Publisher Date	2019-10-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autonomous Driving Vehicle Autonomous Road Vehicle Autonomous Vehicle Deep Reinforcement-learning-based Derived Reinforcement Learning Policy Driver Information System Driving Policy Driving Scenarios Dynamic Programming Knowledge Engineering Technique Learning in AI Manual Driving Vehicle Mixed Driving Environments Optimal Control Optimal Control Method Optimal Policy Optimisation Technique Path Planning Road Traffic Road Traffic System Control Road Vehicle Spatial Variables Control Statistics System Dynamics Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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