Pith. sign in

REVIEW 4 cited by

Language Conditioned Traffic Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2307.07947 v1 pith:Q7QO3OZG submitted 2023-07-16 cs.CV

Shuhan Tan , Boris Ivanovic , Xinshuo Weng , Marco Pavone , Philipp Kraehenbuehl This is my paper · ORCID
classification cs.CV
keywords scenetrafficgenerationlanguagelctgendynamicsmodelsimulators
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CrashAgent: Crash Scenario Generation via Multi-modal Reasoning

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A multi-agent vision-language framework converts NHTSA crash reports into simulation-ready road layouts and collision scenarios, with modest accuracy gains over direct VLM baselines.

  2. Challenger: Affordable Adversarial Driving Video Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A framework for automatic generation of photorealistic adversarial driving videos, shown to sharply increase collision rates of end-to-end autonomous driving models.

  3. Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen

    cs.LG 2025-05 conditional novelty 4.0 of 10

    Crash prediction should learn from near-miss events and synthetic counterfactual scenarios, not just recorded crashes.

  4. Generative AI for Autonomous Driving: A Review

    cs.CV 2025-05 conditional novelty 2.0 of 10

    A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.

Pith tools