Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving...

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Published in:Proceedings (IEEE Conference on Intelligent Transportation Systems) Vol. 2018-November; pp. 2148 - 2155
Main Authors: Hoel, Carl-Johan, Wolff, Krister, Laine, Leo
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2018
Subjects:
Accelerated aging
Artificial Intelligence (cs.AI)
Decision making
Machine Learning (cs.LG)
Markov processes
Neurons
Roads
Robotics (cs.RO)
ISBN:9781728103211, 1728103215
ISSN:2153-0009, 2153-0017, 2153-0017
Online Access:Get full text
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Abstract This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.
AbstractList This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced. https://arxiv.org/abs/1803.10056
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.
Author Wolff, Krister
Laine, Leo
Hoel, Carl-Johan
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  givenname: Leo
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  fullname: Laine, Leo
  email: leo.laine@chalmers.se
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Snippet This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network...
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SubjectTerms Accelerated aging
Artificial Intelligence (cs.AI)
Decision making
Machine Learning (cs.LG)
Markov processes
Neurons
Roads
Robotics (cs.RO)
Title Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
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https://research.chalmers.se/publication/508724
Volume 2018-November
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