用扩散模型生成自动驾驶潜在故障场景，无需外部数据。
Diffusion Models for Safety Validation of Autonomous Driving Systems
- 基于去噪扩散模型，从交通初始状态生成故障案例。
- 在四路交叉口测试中生成多样且真实的风险场景。
- 无需预训练数据或系统先验知识，适合交通路口安全验证。
由于实车测试风险高、成本大，以及潜在故障稀少且多样，自动驾驶系统的安全性验证极为困难。为此，我们训练了一个去噪扩散模型，根据任意初始交通状态生成自动驾驶车辆的潜在故障情况。在四路交叉口问题上的实验表明，该模型能在多种场景下生成真实且多样的故障样本。模型无需外部训练数据，可在有限计算资源下完成训练与推理，且不依赖被测系统的先验知识，适用于交通路口的安全验证。
原文摘要 · Abstract (English)
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capturing a wide variety of potential failures. Our model does not require any external training dataset, can perform training and inference with modest computing resources, and does not assume any prior knowledge of the system under test, with applicability to safety validation for traffic intersections.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。