Language Conditioned Traffic Generation
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:Q7QO3OZGrecord.jsonopen to challenge →
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.
This paper has not been read by Pith yet.
Forward citations
Cited by 7 Pith papers
-
OSC2Runner: OpenSCENARIO 2.x Compliant High-Fidelity AV Simulation in CARLA
OSC2Runner is the first native orchestration framework mapping OpenSCENARIO v2.x DSL to CARLA via a multi-pass transpiler to dynamic behavior trees, claiming tick-by-tick determinism and exact trigger evaluation.
-
RTSGameBench: An RTS Benchmark for Strategic Reasoning by Vision-Language Models
RTSGameBench is a new extensible benchmark for VLMs using diverse RTS matchups, diagnostic mini-games targeting individual competencies, and a self-evolving query-to-game generator, with results showing poor VLM perfo...
-
ParaScale: Scale-Calibrated Camera-Motion Transfer via a Gauge-Invariant Parallax Number
ParaScale extracts a gauge-invariant Parallax Number from a reference video and re-realizes the same parallax against the target scene's depth map to achieve scale-calibrated camera motion transfer.
-
Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles
E² uses transport-regularized sparse control on learned reverse-time SDEs with topology-driven selection and Topological Anchoring to generate realistic adversarial scenarios, improving collision discovery by 9.01% on...
-
AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports
AccidentSim creates videos of car collisions with physically accurate trajectories by simulating data from accident reports, fine-tuning an LM on those trajectories, and rendering with NeRF.
-
Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
-
LLM-based Realistic Safety-Critical Driving Video Generation
Framework uses LLMs for few-shot CARLA scenario code generation focused on collisions, followed by Cosmos-Transfer1 with ControlNet to produce realistic safety-critical driving videos.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.