A curated list of peer-reviewed papers on theoretical and practical aspects of drivers' attention used for paper "Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets" and report on "Behavioral research and practical models of drivers' attention".

Intelligent Driver Monitoring system for Autonomous Vehicles

The "Driver Coach" system is based on a camera, sensors and machine learning algorithms. It monitors car driver behaviour and provides feedback to improve road safety. The system can be part of a larger Road Safety ECO system sharing data between driver and organisations (Smart City).

DMD training framework

An unofficial PyTorch implementation of the 3MDAD dataset

Upon sleep detection, the code will trigger notifications that include a combination of gentle vibrations on the watch and an escalating audio alert on the phone. This approach provides a multi-sensory wake-up cue to increase the user's chance of being roused from sleep.

AI-Powered Driver Monitoring System.

Real-time driver behavior monitoring system using facial landmark detection to prevent accidents caused by fatigue or distraction.

A real-time Raspberry Pi system for detecting driver drowsiness and vehicle crashes using facial landmarks, head pose, and motion sensors. Sends GPS-based emergency SMS alerts via Twilio and displays live driver status on an LCD.

Driver Behaviour Analysis System (DBAS) is a ROS-based driver monitoring system utilizing OpenCV, Dlib, and YOLOv5 to detect and alert on drowsiness, device usage, and other behaviors during driving.

A basic driver monitoring system using cv2 and mediapipe, written in python

A real-time Raspberry Pi system for detecting driver drowsiness and vehicle crashes using facial landmarks, head pose, and motion sensors. Sends GPS-based emergency SMS alerts via Twilio and displays live driver status on an LCD.