Summary of Advdiffuser: Generating Adversarial Safety-critical Driving Scenarios Via Guided Diffusion, by Yuting Xie et al.
AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion
by Yuting Xie, Xianda Guo, Cong Wang, Kunhua Liu, Long Chen
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 paper introduces AdvDiffuser, a novel framework for generating safety-critical scenarios in autonomous driving simulations. Unlike traditional approaches that tailor adjustments to specific systems, AdvDiffuser focuses on transferability by combining a diffusion model with a lightweight guide model. This enables the creation of realistic and diverse scenarios applicable to various tested systems. Experimental results on the nuScenes dataset demonstrate AdvDiffuser’s effectiveness in outperforming existing methods. The framework can be trained using offline driving logs and applied with minimal warm-up episode data, making it a valuable tool for autonomous driving system development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a self-driving car how to avoid accidents. To do this, you need to create fake scenarios that are realistic and challenging. Most people try to make these scenarios specifically for the type of self-driving car they’re working with. But what if we wanted to test multiple cars at once? That’s where AdvDiffuser comes in. It’s a new way to generate these scenarios by using a combination of computer models. This helps create more realistic and varied scenarios that can be used to test different self-driving cars. The results show that this approach is better than what people are doing now. |
Keywords
» Artificial intelligence » Diffusion model » Transferability
