Garg, Deepeka, Chli, Maria and Vogiatzis, George (2019). Traffic3d:A rich 3D-traffic environment to train intelligent agents. IN: Computational Science - ICCS 2019 - 19th International Conference, 2019, Proceedings. Rodrigues, João M.F.; Cardoso, Pedro J.S.; Monteiro, Jânio; Lam, Roberto; Krzhizhanovskaya, Valeria V.; Lees, Michael H.; Sloot, Peter M.A. and Dongarra, Jack J. (eds) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . PRT: Springer.
Abstract
The last few years marked a substantial development in the domain of Deep Reinforcement Learning. However, a crucial and not yet fully achieved objective is to devise intelligent agents which can be successfully taken out of the laboratory and employed in the real world. Intelligent agents that are successfully deployable in true physical settings, require substantial prior exposure to their intended environments. When this is not practical or possible, the agents benefit from being trained and tested on powerful test-beds, effectively replicating the real world. To achieve traffic management at an unprecedented level of efficiency, in this paper, we introduce a significantly richer new traffic simulation environment; Traffic3D. Traffic3D is a unique platform built to effectively simulate and evaluate a variety of 3D-road traffic scenarios, closely mimicking real-world traffic characteristics including faithful simulation of individual vehicle behavior, precise physics of movement and photo-realism. We discuss the merits of Traffic3D in comparison to state-of-the-art traffic-based simulators. Along with deep reinforcement learning, Traffic3D facilitates research across various domains such as object detection and segmentation, unsupervised representation learning, visual question answering, procedural generation, imitation learning and learning by interaction.
Publication DOI: | https://doi.org/10.1007/978-3-030-22750-0_74 |
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Divisions: | College of Engineering & Physical Sciences College of Engineering & Physical Sciences > Systems analytics research institute (SARI) ?? 50811700Jl ?? |
Additional Information: | © Springer Nature B.V. 2019. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-22750-0_74 |
Event Title: | 19th International Conference on Computational Science, ICCS 2019 |
Event Type: | Other |
Event Dates: | 2019-06-12 - 2019-06-14 |
Uncontrolled Keywords: | Deep learning,Intelligent transportation systems,Machine learning,Virtual reality 3D traffic simulator,Theoretical Computer Science,General Computer Science |
ISBN: | 9783030227494 |
Last Modified: | 05 Dec 2024 08:27 |
Date Deposited: | 16 Jul 2019 12:35 |
Full Text Link: | |
Related URLs: |
http://www.scop ... tnerID=8YFLogxK
(Scopus URL) https://link.sp ... -030-22750-0_74 (Publisher URL) |
PURE Output Type: | Conference contribution |
Published Date: | 2019-06-08 |
Accepted Date: | 2019-06-01 |
Authors: |
Garg, Deepeka
Chli, Maria ( 0000-0002-2840-4475) Vogiatzis, George ( 0000-0002-3226-0603) |