Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1364-1370· 0 citations· 17 references
Abstract
Road accidents mostly occur because of driver drowsiness. This paper presents DriveMind, a driver monitoring system that combines MediaPipe Face Mesh-based Eye Aspect Ratio (EAR) analysis with physiological sensors to provide an approximate continuous driver safety score. A multimodal dataset was used to train a regression model, and the trained model is deployed on a Raspberry Pi platform for real-time inference using live sensor data. K-Nearest Neighbors (KNN) regression model is used to provide the driver safety score using multimodal data. The experimental results indicate good performance with MAE of 1.31, RMSE of 1.68 and R2 of 0.913. Raspberry Pi has been selected to deploy the system in real-time inference and cloud-based monitoring, which proves to be applicable in low-cost embedded driver safety applications.
Driver drowsiness and over speeding are major contributors to road accidents worldwide, leading to death and serious injuries. This paper presents a real-time drowsiness and over speedingdetection. The drowsiness detection method uses a Python-based algorithm to calculate the Eye Aspect Ratio (EAR),Mouth Aspect Ratio (...
S. Joshi, A. Tatugade, Prashant Dhoble et al.· 2026 International Conferenc...· 0 citations
Driver drowsiness is a relevant road-safety risk. This paper presents the deployment of a TinyML image classifier on the Seeed Studio XIAO ESP32-S3 Sense. A convolutional neural network was trained on the Driver Drowsiness Dataset using Edge Impulse, quantized to INT8 and integrated into an Arduino firmware. Float32 an...
Willian de Souza Felisberto, R. Marcelino· 2026 IEEE Colombian Conferen...· 0 citations
This paper presents the development of a low-cost, automotive-grade OBD-based driver drowsiness detection system designed for in-use passenger vehicles lacking such a system as a built-in feature. The system leverages real-time vehicle data accessed via the On-Board Diagnostics (OBD) interface to monitor driving behavi...
Abhijit B. Mulay, R. Rashmi, Sreekumar Uthaman et al.· ARAI Journal of Mobility Tec...· 0 citations
One of the leading causes of mortality worldwide is traffic accidents brought on by sleepy drivers, as the driver gradually loses focus without direct awareness. And although there are traditional techniques for monitoring the driver, most of them suffer from limited accuracy or slow response. We created a method to id...
F. J. Kadhim· Al-Noor Journal of Engineeri...· 0 citations
A lightweight dual-MobileNetV2 design with platform-appropriate detectors shows promise for delivering consistent real-time drowsiness alerts across heterogeneous hardware tiers.
Rafi'e· Indonesian Journal of Electr...· 0 citations
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