Smart vehicle black box using IOT and AI

The Smart Vehicle Black Box Using IoT and AI provides a promising approach to improving vehicle safety through the integration of sensor-based monitoring, accident detection, GPS tracking, and IoT communication. By using an ESP32 microcontroller and connected sensors, the system can monitor vehicle movements, identify potentially abnormal conditions, provide local warnings, and communicate available accident-related information to a remote dashboard. GPS tracking can assist in locating a vehicle after a possible accident, while alcohol and seat-belt sensors can support the monitoring of selected driver-safety conditions. The addition of AI-based analysis may improve accident classification when an appropriate model is developed, trained, and validated using representative data. This system has the potential to support faster emergency communication, provide useful information for accident investigation, and encourage safer driving practices. However, its effectiveness depends on proper sensor calibration, reliable power management, accurate event-detection logic, GPS availability, and network connectivity for remote alerts. Future improvements may include secure cloud-based data storage, backup communication methods, advanced driver monitoring, and integration with emergency response services. With further development and thorough testing under different driving conditions, the proposed system could serve as a valuable supporting technology for intelligent vehicle monitoring and road safety.

1.         Singh, Jasspeed, Vengadeshwaran Velu, and Umar Nirmal. “Vehicle Accident Detection System using Internet of Things (VADS-IoT).” In 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 353–359. IEEE, 2021.   2.         Narayanan, K. Lakshmi, C. Ramasamy Sankar Ram, M. Subramanian, R. Santhana Krishnan, and Y. Harold Robinson. “IoT based smart accident detection &insurance claiming system.” In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV),pp. 306–311. IEEE, 2021.   3.         A.A.Rahman,M.I.Siraji,L.I.Khalid,F. Faisal, M.M.Nishat and M. R. Islam, "Detection of Mental State from EEG Signal Data: An Investigation with Machine Learning Classifiers," KST, pp. 152-156, 2022, doi:10.1109/KST53302.2022.9729084.  4.         M. Islam, M. Tabassum, M. M. Nishat, F. Faisal and M.S.Hasan,“Real-TimeClinicalGaitAnalysisandFootAnomaliesDetection Using Pressure Sensor and Convolutional Neural Network” 2022 7th International Conference on Business   and         Industrial Research(ICBIR),IEEE,accepted,inpress.   5.         I. J. Ratul, A. A. Monsur, B. Tabassum, A. M. Ar-Rafi, M. M. Nishatand F. Faisal, “Early risk prediction of cervical cancer:a machine learning approach,” 2022 19th Int. Conf. on Electrical Engg. /Electronics, Comp., Telecomm. And Info. Tech.(ECTI-CON),Huahin, Thailand, accepted, inpress.   6.         Ahmed, S.K.; Mohammed, M.G.; Abdulqadir, S.O.; El-Kader, R.G.A.; El-Shall, N.A.; Chandran, D.; Rehman, M.E.U.; Dhama, K. Road traffic accidental injuries and deaths: A neglected global health issue. Health Sci. Rep. 2023, 6, e1240.   7.         Dange, V.; Patni, A.; Patil, R.; Ponnuru, R.; Pawar, S.; Bhadane, P. IOT based detection of vehicle accident and an emergency help using application interface. Microsyst. Technol. 2023.   8.         Zavantis, D.; Mandalozis, D.; Yasar, A.; Hasimi, L. Automatic Accident Detection System Using IoT Compared to the Systems that a Traffic Centre uses for Accident Detection. Procedia Comput. Sci. 2024.   9.         Bhanote, V.S.; Pandey, A.K.; Iqbal, A.; Swathika, O.G.Smart Vehicle Monitoring and Accident Detection System. Smart Grids Cyber Phys. Syst. Smart Grids Paving Way Smart Cities 2024.   10.     Patil A. S. et al., IoT-based Black Box Monitoring for Vehicle Crash Data Analysis, Research & Reviews: Journal of Embedded Systems & Applications, 2024. Available  at:https://journals.stmjournals.com/rrjoesa/article=2024/v ie w=172085.