title: Language Conditioned Traffic Generation
summary: 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.
paper_authors._id: 64b604e9a98c17892840ec99
paper_authors.name: Shuhan Tan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-18T11:10:54.487Z
paper_authors._id: 64b64debeb9a833e08d079fd
paper_authors.avatarUrl: /avatars/62ad6f5a8c1b69252e855ef26cc4e7c2.svg
paper_authors.fullname: Shuhan Tan
paper_authors.type: user
paper_authors.user: tanshh97
paper_authors._id: 64b604e9a98c17892840ec9a
paper_authors.name: Boris Ivanovic
paper_authors._id: 64b604e9a98c17892840ec9b
paper_authors.name: Xinshuo Weng
paper_authors._id: 64b604e9a98c17892840ec9c
paper_authors.name: Marco Pavone
paper_authors._id: 64b604e9a98c17892840ec9d
paper_authors.name: Philipp Kraehenbuehl
paper_title: Language Conditioned Traffic Generation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: large language model transformer-based decoder dynamic traffic scene generation map locations vehicle dynamics