REVIEW 3 cited by
Generating Driving Scenes with Diffusion
Not yet reviewed by Pith; the record is open.
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
read the original abstract
In this paper we describe a learned method of traffic scene generation designed to simulate the output of the perception system of a self-driving car. In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel combination of diffusion and object detection to directly create realistic and physically plausible arrangements of discrete bounding boxes for agents. We show that our scene generation model is able to adapt to different regions in the US, producing scenarios that capture the intricacies of each region.
Forward citations
Cited by 3 Pith papers
-
Causal-Entity Reflected Egocentric Traffic Accident Video Synthesis
Driver gaze and accident-reason text are used to train a video diffusion model that can edit and generate egocentric crash videos with the correct causal participants, with a new large gaze dataset for accidents.
-
Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation
MuDi-Pro fine-tunes a multi-guided diffusion transformer with DPO using guidance-score preferences to improve controllability of traffic scenario generation on nuScenes.
-
Generative AI for Autonomous Driving: A Review
A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.
Discussion (0). Continue with ORCID to comment.