Traffic3D:A new traffic simulation paradigm


The field of Deep Reinforcement Learning has evolved significantly over the last few years. However, an important and not yet fully-attained goal is to produce intelligent agents which can be successfully taken out of the laboratory and employed in the real-world. Intelligent agents that are successfully deployable in real-world 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 work, we demonstrate a significantly richer new traffic simulation environment; Traffic3D, a platform 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. In addition to deep reinforcement learning, Traffic3D also facilitates research in several other domains such as imitation learning, learning by interaction, visual question answering, object detection and segmentation, unsupervised representation learning and procedural generation.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2019 International Foundation for Autonomous Agents and Multiagent Systems (
Event Title: 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Event Type: Other
Event Dates: 2019-05-13 - 2019-05-17
Uncontrolled Keywords: Intelligent transportation systems,Virtual reality 3d-traffic simulator,Artificial Intelligence,Software,Control and Systems Engineering
ISBN: 978-1-4503-6309-9, 9781510892002
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://dl.acm. ... 3306127.3332110 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2019-05-08
Accepted Date: 2019-05-01
Authors: Garg, Deepeka
Chli, Maria (ORCID Profile 0000-0002-2840-4475)
Vogiatzis, George (ORCID Profile 0000-0002-3226-0603)



Version: Accepted Version

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