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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 02744naa a2200385 4500 | |
| 001 | oai:research.chalmers.se:79cc6429-5d8e-48f4-b923-7f71d91a7707 | |
| 003 | SwePub | |
| 008 | 200608s2020 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://research.chalmers.se/publication/5173692 URI |
| 024 | 7 | a https://doi.org/10.1109/TIV.2019.29559052 DOI |
| 040 | a (SwePub)cth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a art2 swepub-publicationtype |
| 072 | 7 | a ref2 swepub-contenttype |
| 100 | 1 | a Hoel, Carl-Johan,d 1986u Volvo Cars4 aut |
| 245 | 1 0 | a Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving |
| 264 | 1 | c 2020 |
| 520 | a 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. | |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Maskinteknikx Farkostteknik0 (SwePub)203032 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Mechanical Engineeringx Vehicle Engineering0 (SwePub)203032 hsv//eng |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng |
| 653 | a Monte Carlo tree search | |
| 653 | a tactical decision making | |
| 653 | a Autonomous driving | |
| 653 | a reinforcement learning | |
| 700 | 1 | a Driggs-Campbell, Katherineu University of Illinois4 aut |
| 700 | 1 | a Wolff, Krister,d 1969u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)wolff |
| 700 | 1 | a Laine, Leo,d 1972u Volvo Cars4 aut0 (Swepub:cth)leo72 |
| 700 | 1 | a Kochenderfer, Mykel J.u Stanford University4 aut |
| 710 | 2 | a Volvo Carsb University of Illinois4 org |
| 773 | 0 | t IEEE Transactions on Intelligent Vehiclesg 5:2, s. 294-305q 5:2<294-305x 2379-8858 |
| 856 | 4 8 | u https://research.chalmers.se/publication/517369 |
| 856 | 4 8 | u https://doi.org/10.1109/TIV.2019.2955905 |
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