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Combining Planning ...
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
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- Hoel, Carl-Johan, 1986 (författare)
- Volvo Cars
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- Driggs-Campbell, Katherine (författare)
- University of Illinois
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- Wolff, Krister, 1969 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Laine, Leo, 1972 (författare)
- Volvo Cars
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- Kochenderfer, Mykel J. (författare)
- Stanford University
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(creator_code:org_t)
- 2020
- 2020
- Engelska.
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 5:2, s. 294-305
- Relaterad länk:
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This article introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Farkostteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Monte Carlo tree search
- tactical decision making
- Autonomous driving
- reinforcement learning
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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