Traffic3d:A rich 3D-traffic environment to train intelligent agents

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. Dongarra, Jack J.; Rodrigues, João M.F.; Cardoso, Pedro J.S.; Monteiro, Jânio; Lam, Roberto; Krzhizhanovskaya, Valeria V.; Lees, Michael H. and Sloot, Peter M.A. (eds) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer-Verlag Wien.

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
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences
Engineering & Applied Sciences > Systems analytics research institute (SARI)
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,Computer Science(all)
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... -030-22750-0_74 (Publisher URL)
Published Date: 2019-06-08
Authors: Garg, Deepeka
Chli, Maria ( 0000-0002-2840-4475)
Vogiatzis, George ( 0000-0002-3226-0603)

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Version: Accepted Version

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