# Intellifotainment assist” – Smart HMI for passenger cars To run the program, download all files and save them in the same directory. After that, simply run 'Master.py' in the terminal. At the moment, the program will only run in linux based systems. # The Idea Infotainment systems have come a long way since the first set of dashboards installed in cars. Through our idea, we aim to create a Human Machine interaction model that takes infotainment systems to a new level. The driver tends to get distracted from the road while performing secondary tasks such as changing the music track, locking/unlocking the door while driving etc. Our system shall enable the driver to focus only on driving. Controlling the secondary tasks will be much easier. Our product primarily comprises of 5 modules: 1) Attention and drowsiness detection: - A camera shall be present on the dashboard, in front of the driver, behind the steering wheel. Through digital image processing techniques , using hough circle algorithm and haarcascade of an eye, we shall keep track of the driver’s sight. If he or she is looking away from the road while driving for more than a specified amount of time, we shall alert the driver to focus. We shall map the head orientation and iris position to accurately identify the driver’s attention. 2) Infotainment control features using blink combination: Through a combination of blinks, the driver can turn on or of the headlights, tail lights as well as indicators. Blinking of the eyes shall be detected using ‘dlib’ features in python. This shall give extremely accurate results. 3) Voice commands to control wipers, car lock, music system and windows A simple, yet extremely useful idea that would make the life of the driver a whole lot easy. Enabling the driver to speak to his car infotainment system would allow him to control and navigate these functionalities with great ease. The car will be enabled with a virtual assistant. 4) Automatic rear view mirror adjustment scheme: Using the camera placed in front of the driver, the system shall detect the position of the driver’s head. This shall also be done using image processing techniques and we shall identify the coordinates of the driver head in 3D space. There will be a mapping between the head position and mirror adjustment scheme. The mirrors will adjust their position using servo motors and shall do so automatically by identifying the head position. 5) A revolutionary reverse-assistance algorithm for smart parking and general reversing: Probably the highlight of our model, this feature shall make driving the car in and out of a parking spot, or rather, even reversing a car in general, far easier and safer than what it already is. Like most other modern cars, our model shall also have a camera installed at the back and the corresponding image displayed at the infotainment screen for parking assistance. Upon activating the reverse gear, the screen shall trace the line of motion of the car corresponding to the current position of the steering wheel. Because of this feature, the driver gets an idea of whether or not he’ll hit an obstacle while reversing if the steering wheel is kept at that position. Taking this feature to another level, the rear camera, after capturing the live video feed from the back of the car, shall perform image processing and machine learning algorithms to find a safe, obstacle-free path for reversing and indicate the driver to move the steering wheel accordingly. So instead of relying solely on the drivers judgement, our system shall actually find the path to be taken while reversing, such that other cars and other obstacles will be avoided, and accordingly recommend the driver to steer the wheel in that direction. This feature shall be extremely useful for new drivers/ learners. During the initial phase, to prevent errors from creeping in, we will always have a manual override button. After a good amount of testing, further modifications and refinements can be made. Our systems adds new dimensions to both precautionary safety measures, as well as convenience. If implemented properly, we are confident that our project will reach new heights of HMI and driver assistance technology. It will give drivers several less reasons to worry about.

This repo contains the source code for smart driver monitoring system. Currently, it supports Object detection such as mobile handset, wine glass, bottle etc., Head-pose estimation, drowsiness detection, iris landmarks estimation.

Developed a Driver Drowsiness Detection System using Raspberry Pi 4 and YOLO-based image processing to monitor eye state in real time. The system detects drowsiness by analyzing prolonged eye closure and triggers alerts to improve driver safety. Combined computer vision, deep learning, and embedded hardware into a cost-effective solution.

This project is a Drowsiness Detection Dashboard that uses computer vision and AI to monitor a driver’s alertness in real time. It employs OpenCV and MediaPipe Face Mesh to analyze eye and mouth movements, calculating EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) to detect signs of sleepiness or yawning.

In India, many accidents occur every day due to breakdowns. As the population grows, so does the number of cars and accidents. Most accidents occur because people feel drowsy, speedy, drunk, or driving. Therefore, we will analyze the data of the past few years, investigate the main causes of accidents, and introduce an accident prevention system to deal with the problem of accidents caused by human sleep and drowsiness. In India, many accidents occur every day due to breakdowns. No. As the population grows, so does the number of cars and accidents. Therefore, we will introduce an accident prevention system to deal with the problem of accidents caused by drowsiness and drowsiness. As computer vision technology advances and advances, smart / smart cameras are being developed that predict driver fatigue and warn drivers. This can reduce accidents when the driver is tired. This task uses deep learning to follow a new approach to detect driver drowsiness based on the condition of the eyes while driving the vehicle. Histogram equalization and cany edge detection algorithms are used in this task to detect faces and extract eye areas from face images.

Smart Car System for Driver Authentication and Drowsiness Detection

A smart IoT-based Driver Drowsiness Detection System built using ESP32-CAM, AI, and Supabase, capable of image capture, and Telegram alerts with integrated cloud dashboard.

Smart Fleet Monitoring System with real-time tracking and driver drowsiness detection. Automatically updates route data, detects deviations, and sends alerts to improve safety and efficiency in transportation.

The AI-powered Driver Drowsiness Detection System monitors fatigue using a webcam, OpenCV for facial tracking, and a CNN model to classify drowsy states. Built with Python, TensorFlow, and Keras, it triggers alerts to prevent accidents, enhancing road safety and enabling smart vehicle integration. 🚀

ChatGPT said: The Driver Drowsiness Detection System uses AI and computer vision to monitor a driver’s eyes and facial features in real time. It detects signs of fatigue like blinking or yawning and triggers instant alerts, helping prevent accidents through a smart, affordable, and non-invasive solution.

SMART DRIVER DROWSINESS DETECTION SYSTEM USING DEEP LEARNING

Developed a real-time system using MediaPipe and a webcam to detect driver drowsiness via eye closure, incorporating an audio alert for safety.

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IoT-Based Smart Alert System for Drowsy Driver Detection.

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A smart Driver Assistance System (ADAS) using Arduino sensors and Android-based AI Drowsiness Detection.

Drowsiness detection systems aim to prevent accidents by monitoring driver fatigue signs like eye closure, yawning, and head position via image processing and AI. PERCLOS (percentage of eyelid closure over time) helps detect drowsiness early. Challenges include lighting, accuracy, and false positives.

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Smart security car using Raspberry Pi with remote control, face recognition, driver drowsiness detection, ultrasonic collision sensor, and smoke detection system.

Smart Alert detection system is Designed and developed a smart alert detection system model to analyze the driver drowsiness and to predict the drowsiness of the Driver and we added some additional changes to our project like when the drowsiness detected more than two times it will give the control to safe auto parking for the Human Safety.

AI Driver Drowsiness Detection System is a smart safety system that uses AI to detect driver fatigue, alcohol consumption, and seat belt usage. It alerts the driver to prevent accidents and improve road safety.

AI powered driver drowsiness detection system and voice assistant using google gemini that detects driver drowsiness using computer vision and prevents accidents with real-time alerts. It also features a hands-free voice assistant powered by the Google Gemini API, enabling safe interaction, navigation, and smart assistance while driving.

A smart IoT-driven system enhancing driver safety through alcohol detection, drowsiness monitoring, signboard recognition, light control, and accident alert mechanisms. This repository includes the source code, implementation details, and supporting documentation.

A real-time smart drowsiness detection system that alerts drivers or users when signs of drowsiness or inattentiveness are detected using facial landmarks and EAR (Eye Aspect Ratio). The project uses an IP webcam for camera feed input and includes a GUI built with PyQt5.

Smart Ambulance Services is a real-time ambulance booking and tracking system that allows users to request ambulances, track their assigned driver, and ensure timely medical assistance. The system includes an admin panel for managing bookings, driver assignments, and IoT-based safety features like driver drowsiness detection.

Python-based driver eye detection system using computer vision to monitor drowsiness in real time. Detects eye closure and alerts the driver to prevent accidents. Built with OpenCV and facial landmark detection, it enhances road safety by providing a smart, automated fatigue monitoring solution.

A real-time drowsiness detection system for drivers that not only alerts when fatigue is detected but also smartly opens Google Maps to suggest nearby hotels - helping drivers find a safe spot to rest and stay alert on the road. Designed to promote road safety through smart, responsive assistance.

The AI-powered Driver Drowsiness Detection System monitors fatigue using a webcam, OpenCV for facial tracking, and a CNN model to classify drowsy states. Built with Python, TensorFlow, and Keras, it triggers alerts to prevent accidents, enhancing road safety and enabling smart vehicle integration. 🚀

Accident Prevention : A smart accident prevention system using AI, IoT, and real-time data to enhance road safety. It detects obstacles, monitors driver behavior, and provides collision alerts. The system also tracks weather and road conditions to prevent accidents. Drowsiness detection ensures driver awareness, reducing fatigue-related crashes.