Language Conditioned Traffic Generation
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.
64b604e9a98c17892840ec99
Shuhan Tan
claimed_verified
2023-07-18T11:10:54.487Z
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/avatars/62ad6f5a8c1b69252e855ef26cc4e7c2.svg
Shuhan Tan
user
tanshh97
64b604e9a98c17892840ec9a
Boris Ivanovic
64b604e9a98c17892840ec9b
Xinshuo Weng
64b604e9a98c17892840ec9c
Marco Pavone
64b604e9a98c17892840ec9d
Philipp Kraehenbuehl
Language Conditioned Traffic Generation
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.
LCTGen, a combination of a large language model and transformer-based decoder, generates realistic dynamic traffic scenes by selecting map locations and modeling vehicle dynamics.
large language model transformer-based decoder dynamic traffic scene generation map locations vehicle dynamics