mlsanityData to 14 Oct 2026

Diffusion Models for Safety Validation of Autonomous Driving Systems

arXiv 2506.08459 ↗First posted 10 Jun 2025cs.RO cs.AIcited by 1

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 12 of its 24 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

12 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems20 Jun
  2. 2023SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries31 Dec

Other references · 10

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  5. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  6. 2023Adversarial Diffusion Distillationcited by 759
  7. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  8. 2019Adaptive Stress Testing for Autonomous Vehiclescited by 71
  9. 2020Interpretable Safety Validation for Autonomous Vehiclescited by 7
  10. 2023Simulation-based Validation for Autonomous Driving Systemscited by 6

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Robust Planning for Autonomous Vehicles with Diffusion-Based Failure Samplerscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Data-driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations
  2. 2025Controllable Latent Diffusion for Traffic Simulation
  3. 2025Safety-Critical Traffic Simulation with Guided Latent Diffusion Model
  4. 2025AUTHENTICATION: Identifying Rare Failure Modes in Autonomous Vehicle Perception Systems using Adversarially Guided Diffusion Models
  5. 2024AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion
  6. 2024Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models
  7. 2026PC-Diffuser: Path-Consistent Capsule CBF Safety Filtering for Diffusion-Based Trajectory Planner
  8. 2026BridgeGuard: Explicit Safety Drift for Diffusion-based Autonomous Driving