TY - GEN
T1 - Traffic3d
T2 - 19th International Conference on Computational Science, ICCS 2019
AU - Garg, Deepeka
AU - Chli, Maria
AU - Vogiatzis, George
N1 - © 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
PY - 2019/6/8
Y1 - 2019/6/8
N2 - 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.
AB - 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.
KW - Deep learning
KW - Intelligent transportation systems
KW - Machine learning
KW - Virtual reality 3D traffic simulator
UR - http://www.scopus.com/inward/record.url?scp=85068448575&partnerID=8YFLogxK
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-22750-0_74
U2 - 10.1007/978-3-030-22750-0_74
DO - 10.1007/978-3-030-22750-0_74
M3 - Conference publication
AN - SCOPUS:85068448575
SN - 9783030227494
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 749
EP - 755
BT - Computational Science - ICCS 2019 - 19th International Conference, 2019, Proceedings
A2 - Rodrigues, João M.F.
A2 - Cardoso, Pedro J.S.
A2 - Monteiro, Jânio
A2 - Lam, Roberto
A2 - Krzhizhanovskaya, Valeria V.
A2 - Lees, Michael H.
A2 - Sloot, Peter M.A.
A2 - Dongarra, Jack J.
PB - Springer
Y2 - 12 June 2019 through 14 June 2019
ER -