Summary of Scenediffuser: Efficient and Controllable Driving Simulation Initialization and Rollout, by Chiyu Max Jiang et al.
SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout
by Chiyu Max Jiang, Yijing Bai, Andre Cornman, Christopher Davis, Xiukun Huang, Hong Jeon, Sakshum Kulshrestha, John Lambert, Shuangyu Li, Xuanyu Zhou, Carlos Fuertes, Chang Yuan, Mingxing Tan, Yin Zhou, Dragomir Anguelov
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The SceneDiffuser is a novel scene-level diffusion prior designed for traffic simulation in autonomous vehicle development. It addresses two key stages: scene initialization and scene rollout. While diffusion models have been effective in learning realistic agent distributions, they face challenges like controllability, realism, and inference efficiency. To address these issues, the authors introduce amortized diffusion for simulation, which reduces inference steps by 16x while mitigating closed-loop errors. They also enhance controllability through generalized hard constraints and language-based constrained scene generation via few-shot prompting of a large language model (LLM). The approach is tested on the Waymo Open Sim Agents Challenge, achieving top open-loop performance and best closed-loop performance among diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SceneDiffuser is a new tool for simulating realistic traffic scenes. It helps make self-driving cars more realistic and efficient. The challenge is to create scenes that look real while also making sure the virtual agents in those scenes behave realistically. To solve this, the team created a special kind of “denoising” that makes the simulation faster and better. They also added rules for the virtual agents to follow, so they don’t get too crazy or unpredictable. The new method was tested on a big challenge and performed really well. |
Keywords
» Artificial intelligence » Diffusion » Few shot » Inference » Large language model » Prompting
