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@article{190182,
        author = {Nice Rose C J and Lakshmi M S and Reshmi Sathian P and Udhayakrishna E M and Rebitha K R},
        title = {AI-Based Road Accident Detection and Emergency Response Systems: A Systematic Survey of Vision-Driven and Intelligent Transportation Approaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5132-5136},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190182},
        abstract = {Road traffic accidents continue to be a critical global safety challenge, causing millions of fatalities and severe injuries annually due to delayed detection and inefficient emergency response mechanisms. Existing traffic monitoring and dispatch systems often fail to provide timely intervention, particularly in densely populated urban environments and high-speed road networks.
The objective of this survey is to systematically review Artificial Intelligence (AI) and Machine Learning (ML) based approaches for automatic road accident detection and emergency response. The study focuses on analysing vision-based detection systems, traffic data analytics platforms, and intelligent emergency management frameworks reported in the literature.
Research papers published between 2015 and 2025 were collected from IEEE Xplore, SpringerLink, ScienceDirect, and Google Scholar using keywords related to accident detection, computer vision, intelligent transportation systems, and emergency response. Relevant studies were selected based on methodological rigor and applicability to real-world scenarios.
The survey findings indicate that CNN-based vision systems, real-time traffic analytics, and AI-assisted emergency dispatch platforms significantly improve detection accuracy and reduce response time. However, challenges such as lack of real-time deployment, limited datasets, poor model explainability, and weak system integration remain unresolved.
Future research should emphasize unified, explainable, and real-time AI-driven frameworks that seamlessly integrate accident detection with intelligent emergency response to enhance road safety and save lives.},
        keywords = {Road Accident Detection, Artificial Intelligence, Deep Learning, Emergency Response, Intelligent Transportation Systems},
        month = {January},
        }