Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes
CoRR(2023)
摘要
We advocate the idea of the natural-language-driven(NLD) simulation to
efficiently produce the object interactions between multiple objects in the
virtual road scenes, for teaching and testing the autonomous driving systems
that should take quick action to avoid collision with obstacles with
unpredictable motions. The NLD simulation allows the brief natural-language
description to control the object interactions, significantly reducing the
human efforts for creating a large amount of interaction data. To facilitate
the research of NLD simulation, we collect the Language-to-Interaction(L2I)
benchmark dataset with 120,000 natural-language descriptions of object
interactions in 6 common types of road topologies. Each description is
associated with the programming code, which the graphic render can use to
visually reconstruct the object interactions in the virtual scenes. As a
methodology contribution, we design SimCopilot to translate the interaction
descriptions to the renderable code. We use the L2I dataset to evaluate
SimCopilot's abilities to control the object motions, generate complex
interactions, and generalize interactions across road topologies. The L2I
dataset and the evaluation results motivate the relevant research of the NLD
simulation.
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