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

2025-10-04

AI Summary

To address insufficient accident detection accuracy in complex scenarios within intelligent transportation systems, this paper proposes a hybrid framework integrating diffusion models with guided classification. Methodologically, we design a multi-condition modulation mechanism that jointly leverages temporal embeddings and image covariate embeddings to dynamically control the diffusion process; incorporate fine-tuned ExceptionNet outputs as semantic conditions; and enhance classification robustness via a linear projection-based modulation module. Additionally, we optimize step scheduling and encoding strategies. The framework enables efficient, scalable deployment on cloud platforms. Evaluated on public benchmarks, it achieves 97.32% accuracy—significantly outperforming state-of-the-art baselines—demonstrating the effectiveness and advancement of synergistically modeling conditional diffusion generation and discriminative classification for image-driven traffic accident detection.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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%.

Problem

• Develops cloud-based diffusion model for accident detection
• Enhances classification accuracy in intelligent transportation systems
• Overcomes computational limitations through scalable cloud processing

Innovation

• Hybrid model combines guidance classification with diffusion techniques
• Cloud-based implementation enables scalable and efficient processing
• Multiple conditional modules dynamically adapt network behavior during diffusion