Plug-and-play multi-sensor Architecture for Monitoring Biophysical and Postural Parameters of a Connected Vehicle's Driver in a smart-road Scenario

Driver health monitoring is a key element in improving road safety, as the human factor continues to contribute significantly to accidents, despite advances in driver assistance systems. Fatigue, distraction, drowsiness, drunkness, and stress can compromise the driver’s perception and decision-making, increasing the risk of errors. The ACTIVE-EV project aims to enhance driver safety by implementing a monitoring architecture in the cockpit to assess the driver’s psycho-physical condition and alert nearby vehicles via V2X (Vehicle-to-Everything) communication, thereby improving urban mobility in a smart road scenario. The architecture includes BLE-based plug-and-play solutions that require no wiring or modifications to the vehicle, specifically, sensorized covers for the steering wheel, safety belt, and seat, each equipped with sensors to acquire biophysical, inertial, and postural signals. Each solution, powered by a rechargeable battery, integrates a microcontroller-based module for local pre-processing and data transmission via BLE to a Raspberry Pi 5 board, which by Machine Learning (ML) and Deep Learning (DL) algorithms and sensor fusion techniques, determines the driver’s condition. When a potentially dangerous state is detected, the system provides acoustic and visual warnings to the driver and sends real-time alert messages to roadside infrastructure and vehicles.