# A Novel Cloud-Based Diffusion-Guided Hybrid Model for High-Accuracy Accident Detection in Intelligent Transportation Systems

arXiv: https://arxiv.org/abs/2510.03675 · HTML: https://mlsanity.com/paper/2510.03675/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions](https://mlsanity.com/paper/2102.05379/) (2021)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Denoising Diffusion Models for Plug-and-Play Image Restoration](https://mlsanity.com/paper/2305.08995/) (2023)
- [DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents](https://mlsanity.com/paper/2201.00308/) (2022)
- [CARD: Classification and Regression Diffusion Models](https://mlsanity.com/paper/2206.07275/) (2022)
- [Towards Explainable Traffic Flow Prediction with Large Language Models](https://mlsanity.com/paper/2404.02937/) (2024)
- [Multimodal LLM for Intelligent Transportation Systems](https://mlsanity.com/paper/2412.11683/) (2024)
- [Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.10101/) (2021)
- [Embracing Large Language Models in Traffic Flow Forecasting](https://mlsanity.com/paper/2412.12201/) (2024)
- [Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2406.11941/) (2024)
- [Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysis](https://mlsanity.com/paper/2506.16186/) (2025)

## Abstract (arXiv)

The integration of Diffusion Models into Intelligent Transportation Systems (ITS) is a substantial improvement in the detection of accidents. We present a novel hybrid model integrating guidance classification with diffusion techniques. By leveraging fine-tuned ExceptionNet architecture outputs as input for our proposed diffusion model and processing image tensors as our conditioning, our approach creates a robust classification framework. Our model consists of multiple conditional modules, which aim to modulate the linear projection of inputs using time embeddings and image covariate embeddings, allowing the network to adapt its behavior dynamically throughout the diffusion process. To address the computationally intensive nature of diffusion models, our implementation is cloud-based, enabling scalable and efficient processing. Our strategy overcomes the shortcomings of conventional classification approaches by leveraging diffusion models inherent capacity to effectively understand complicated data distributions. We investigate important diffusion characteristics, such as timestep schedulers, timestep encoding techniques, timestep count, and architectural design changes, using a thorough ablation study, and have conducted a comprehensive evaluation of the proposed model against the baseline models on a publicly available dataset. The proposed diffusion model performs best in image-based accident detection with an accuracy of 97.32%.