Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)

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AI Based Drone for Accident Detection and Evidence Capture Systems

Authors
P. Gowtham1, R. Kavinkumar2, *, M. Hemanth3, K. S. Bhavananth Sri4
1Associate Professor, Department of Electronics and Communication Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India
2UG Scholar, Department of Electronics and Communication Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India
3UG Scholar, Department of Electronics and Communication Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India
4UG Scholar, Department of Electronics and Communication Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, India
*Corresponding author. Email: kit27.ecea048@gmail.com
Corresponding Author
R. Kavinkumar
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-616-6_66How to use a DOI?
Keywords
low-cost system; Accident Detection; ESP32 Microcontroller; IOT; MPU6050 Sensor; Autonomous UAV; Drone-Based Evidence Capture
Abstract

One of the leading causes of death is caused by accidents, which frequently lead to delayed emergency services and evidence gathering for investigation. In this proposed paper uses embedded control technologies and the Internet of Things (IOT) in collision detection and drone based evidence capture system that address the issues and the UAVs fabricated in lowcost system. This system uses an ESP32 Microcontroller with a motion and tilt sensor (MPU6050) to detect the changes in acceleration between vehicles. Using AI for autonomous navigation to take decision by own, with a high-resolution camera is triggered by the ESP32 when it detects an accident. The drone takes off and records live footage of the scene. After taken the pictures and videos are sent to a cloud-based dashboard for monitoring, analysis, and evidence storage via wireless communication modules. A Lithium-ion battery controlled by a TP4056 charging circuit powers the entire system and MOSFET-based motor drivers provide accurate and effective control over the drone’s propulsion. In the end, this system contributes to security in road safety and post-accident investigation by improving the efficiency of accident detection, accuracy of evidence gathering, and speed of emergency response through the combination of real-time sensing, autonomous evidence collection.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 March 2026
ISBN
978-94-6239-616-6
ISSN
1951-6851
DOI
10.2991/978-94-6239-616-6_66How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Gowtham
AU  - R. Kavinkumar
AU  - M. Hemanth
AU  - K. S. Bhavananth Sri
PY  - 2026
DA  - 2026/03/31
TI  - AI Based Drone for Accident Detection and Evidence Capture Systems
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 884
EP  - 896
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_66
DO  - 10.2991/978-94-6239-616-6_66
ID  - Gowtham2026
ER  -