Summary of Regents: Real-world Safety-critical Driving Scenario Generation Made Stable, by Yuan Yin et al.
ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
by Yuan Yin, Pegah Khayatan, Éloi Zablocki, Alexandre Boulch, Matthieu Cord
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 proposed ReGentS approach generates safety-critical driving scenarios by modifying complex real-world regular scenarios through trajectory optimization. This addresses the limitations of large-scale deployment in autonomous driving systems due to rare real-world data. The method stabilizes generated trajectories, introduces heuristics for collision avoidance, and handles up to 32 agents. It also simplifies gradient descent-based optimization using a differentiable simulator. This enables future advancements in robust planner training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving systems face challenges with safety-critical scenarios that are rare in real-world data. To address this, researchers developed a way to generate these scenarios by modifying complex regular scenarios. They created an approach called ReGentS, which makes sure the generated trajectories are stable and avoids obvious collisions. This helps train better autonomous vehicles. |
Keywords
» Artificial intelligence » Gradient descent » Optimization
