Open Access

IOT and AI-Based Automatic Crash Detection Systems for Emergency Services: A Review

https://doi.org/10.55640/irjaet-v03i07-03

pdf

Dr. Parth Gautam ¹ ,

⁴ Assistant Professor, Department of Computer Sciences and Applications, Mandsaur University, Mandsaur, India

Abstract

Road traffic collisions are a serious public safety issue that continues to cause high rates of death, injury and economic losses around the world. Reporting systems for accidents are frequently inefficient and slow, especially in low-traffic regions and remote areas, which can delay the response to an accident. With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), computer vision and intelligent transportation systems, automatic crash detection systems have been developed that have ability to monitor in real-time and report the crash quickly. A comprehensive review of the various IoT and AI-based crash detection technologies is provided including the sensing hardware, communication infrastructure, cloud and fog computing, computer vision and machine learning techniques. It reviews the latest studies on using accelerometers, GPS, surveillance cameras, CNN and YOLO-based models to detect accidents, and offers a comparative assessment of their techniques, advantages, obstacles, and suggestions. There are several research gaps identified in the review, such as limited accident severity assessment, limited multimodal sensor fusion, and scalability issues; interoperability problems, privacy concerns, and real-world validation problems in different traffic environments. Based on these findings, future research directions for the paper are discussed, focusing on Explainable AI, Real-time inference at the edge, Secure IoT communication and Scalable intelligent transportation frameworks. The study illustrates how the synergy between AI technologies and IoT-enabled sensing and communication capabilities can significantly improve the capabilities of accident detection, reduce emergency response times and contribute to overall road safety.

Keywords

Road Accident Detection, Intelligent Transportation Systems (ITS), Internet of Things (IoT)

References

H. K. Yadav, “Design and Implement Machine Learning Based Predictive Analytics for Road Safety and Accident Prevention,” in 2026 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IATMSI68868.2026.11465965.

N. Pathik, R. K. Gupta, Y. Sahu, A. Sharma, M. Masud, and M. Baz, “AI-Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities,” Sustain., vol. 14, no. 13, 2022, doi: 10.3390/su14137701.

J. Tao, R. Ali, S. Ahmad, and F. Ali, “IoT-Based Smart Accident Detection and Early Warning System for Emergency Response and Risk Management,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 3, pp. 660–673, 2025, doi: 10.14569/ijacsa.2025.0160365.

M. Sudhan, P. Venkadesh, and S. V. Divya, “AetherGuard - AI-Powered IoT Emergency Response System for Smart Vehicle Collision Detection and Ambulance Dispatch,” Int. J. Res. Publ. Rev., vol. 6, no. 4, pp. 2618–2623, 2025, doi: 10.55248/gengpi.6.0425.1403.

S. Olugbade, S. Ojo, A. L. Imoize, J. Isabona, and M. O. Alaba, “A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems,” Math. Comput. Appl., vol. 27, no. 5, p. 77, 2022, doi: 10.3390/mca27050077.

N. Adik, “The Rise of Open and Free Networks: A Community-Driven Paradigm for Decentralized Connectivity,” in 2025 IEEE First International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS), Coimbatore, India: IEEE, 2025, pp. 1–9, November. doi: 10.1109/ICINVENTS64613.2025.11402224.

S. Singamsetty, “AI-powered Satellite Image Processing for Global Air Traffic Surveillance Techniques Using NCNN–EGSA Optimization Techniques,” in Machine Learning Based Air Traffic Surveillance System Using Image Processing, Emerald Publishing Limited, 2026, pp. 179–197. doi: 10.1108/978-1-80592-062-520251010.

P. Dagade, P. Salunke, S. Salunke, and S. T. Patil, “Accident Detection & Ambulance Rescue System Using Wireless Technology,” Int. Res. J. Eng. Technol., vol. 4, no. 5, pp. 1324–1326, 2017.

H. P. Cyril and S. Kumara, “Identification of Anomalies via Deep Learning-Based Models for High-Dimensional Telecom Traffic Data,” J. Adv. Artif. Intell., vol. 4, no. 1, pp. 24–37, 2026.

C. Thompson, J. White, B. Dougherty, A. Albright, and D. C. Schmidt, “Using smartphones to detect car accidents and provide situational awareness to emergency responders,” Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng., vol. 48 LNICST, pp. 29–42, 2010, doi: 10.1007/978-3-642-17758-3_3.

V. K. Sharma, “Enabling Mission-Critical Communication via VoLTE for Public Safety Networks,” J. Adv. Dev. Res., vol. 10, no. 1, pp. 1–10, June, 2019.

Q. Zhou and J. Zhang, “Research prospect of Internet of Things Geography,” in 2011 19th International Conference on Geoinformatics, 2011, pp. 1–5. doi: 10.1109/GeoInformatics.2011.5981045.

T. Banerjee and H. Singh, “Securing Non-Human Identities in Industrial IoT a Blockchain-Based Trust Framework,” NIPES - J. Sci. Technol. Res., vol. 7, no. 4, pp. 228–246, Dec. 2025, doi: 10.37933/nipes/7.4.2025.1660.

F. Bhatti, M. A. Shah, C. Maple, and S. U. Islam, “A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments,” Sensors, vol. 19, no. 9, p. 2071, May 2019, doi: 10.3390/s19092071.

Y. Muni, “Understanding The Evolution Of Internet Protocols: An In-Depth Review Of Ipv4 And Ipv6: A Comparative Review Of Transition Challenges And Solutions,” Int. J. Adv. Res. Comput. Sci., vol. 16, no. 4, pp. 109–117, Aug. 2025, doi: 10.26483/ijarcs.v16i4.7307.

J. White, C. Thompson, H. Turner, B. Dougherty, and D. Schmidt, “WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones,” MONET, vol. 16, pp. 285–303, 2011, doi: 10.1007/s11036-011-0304-8.

H. Ravilla, “Predictive Analytics for Customer Churn in Salesforce Service Cloud,” in Learning and Analytics in Intelligent Systems, 2026, pp. 14–24. doi: 10.1007/978-3-032-05377-0_2.

H. N. Dholariya, “Human-in-the-Loop AI for Cloud Data Engineering: The Collaborative Intelligence Architecture (CIRA) for Regulated Industries,” J. Inf. Syst. Eng. Manag., vol. 11, no. 1, pp. 883–898, Jan, 2026.

K. Dixit, “An Intelligent Decision Support System for Tourist Management Via Global Tourism Analysis,” Int. J. Sci. Res. Sci. Technol., vol. 11, no. 4, pp. 755–765, oct. 2024.

E. Alreshidi, “Introducing Fog Computing (FC) Technology to Internet of Things (IoT) Cloud-Based Anti-Theft Vehicles Solutions,” Int. J. Syst. Dyn. Appl., vol. 11, pp. 1–21, 2022, doi: 10.4018/IJSDA.287114.

E. Nasr, E. Kfoury, and D. Khoury, “An IoT approach to vehicle accident detection, reporting, and navigation,” 2016, pp. 231–236. doi: 10.1109/IMCET.2016.7777457.

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and Its Role in the Internet of Things Characterization of Fog Computing,” MCC’12 Proc. first Ed. MCC Work. Mob. Cloud Comput., pp. 13–16, 2012.

S. Kale and P. Patil, “Data Mining Technology with Fuzzy Logic, Neural Networks and Machine Learning for Agriculture: Proceedings of ICDMAI 2018, Volume 2,” in Advances in Intelligent Systems and Computing, 2019, pp. 79–87. doi: 10.1007/978-981-13-1274-8_6.

S. K. Chandrappa, S. Paheding, A. A. Reyes-Angulo, and A. Essa, “Attention-Based Spectral Profile Representation for Hyperspectral Image Classification,” in NAECON 2025 - IEEE National Aerospace and Electronics Conference, IEEE, Jul. 2025, pp. 1–6. doi: 10.1109/NAECON65708.2025.11235401.

E. Bhowmik, P. Bhardwaj, A. U. Khan, D. K. Behera, K. S. Chaudhury, and B. Kar, “Automatic Vehicle Accident Detection and Classification from Images Using YOLOv8,” in 2026 International Conference on Intelligent Computing, Networks, and Security (IC-ICNS), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/IC-ICNS68863.2026.11537436.

A. Sandvik and J. Zhang, “Leveraging Machine Learning Algorithms in Two Car Crash Detection Systems on a Custom Dataset,” in SoutheastCon 2026, IEEE, Feb. 2026, pp. 1–8. doi: 10.1109/SoutheastCon63549.2026.11476699.

V. Koreddi, A. N. Santhoshini, C. P. Sai Raj, S. Sadhik, and T. Keerthana, “AI-Powered Smart Helmet Detection System for Traffic Safety,” in 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), IEEE, Aug. 2025, pp. 588–593. doi: 10.1109/ICSCSA66339.2025.11171366.

M. A. Sahraei and S. R. M. Al Mamari, “A Review of Internet of Things Approaches for Vehicle Accident Detection and Emergency Notification,” 2025. doi: 10.3390/su17146510.

S. S. Kushawah, A. Gupta, and R. Gupta, “Smart Road Surveillance: An AI and ML-Based System for Automatic Accident Detection and Emergency Response,” in 2024 International Conference on Cybernation and Computation (CYBERCOM), 2024, pp. 776–780. doi: 10.1109/CYBERCOM63683.2024.10803237.

C. R. Prabu and K. Namitha, “IoT-based automatic road accident detection systems: A review,” in Proceedings - 2024 4th International Conference on Soft Computing for Security Applications, ICSCSA 2024, 2024. doi: 10.1109/ICSCSA64454.2024.00122.

A. H. Alkhaiwani and B. S. Alsamani, “A Framework and IoT-Based Accident Detection System to Securely Report an Accident and the Driver’s Private Information,” Sustain., 2023, doi: 10.3390/su15108314.

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>

International Research Journal of Advanced Engineering and Technology

IOT and AI-Based Automatic Crash Detection Systems for Emergency Services: A Review

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles