Language Conditioned Traffic Generation
Shuhan Tan Boris Ivanovic Xinshuo Weng Marco Pavone Philipp Kraehenbuehl
Simulation forms the backbone of modern self-driving development. Simulators
help develop, test, and improve driving systems without putting humans,
vehicles, or their environment at risk. However, simulators face a major
challenge: They rely on realistic, scalable, yet interesting content. While
recent advances in rendering and scene reconstruction make great strides in
creating static scene assets, modeling their layout, dynamics, and behaviors
remains challenging. In this work, we turn to language as a source of
supervision for dynamic traffic scene generation. Our model, LCTGen, combines a
large language model with a transformer-based decoder architecture that selects
likely map locations from a dataset of maps, and produces an initial traffic
distribution, as well as the dynamics of each vehicle. LCTGen outperforms prior
work in both unconditional and conditional traffic scene generation in terms of
realism and fidelity. Code and video will be available at
https://ariostgx.github.io/lctgen.