Traffic3D:A new traffic simulation paradigm

Abstract

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.