Advance driver assistance system

Project summary:   ADAS: Advance Driver Assistance System This is AI based solution to monitor drive in-cabin and out-cabin In in-cabin mode driver will be monitored, his authentication (face recognition if the driver is authorized), and driver monitoring like if dri

Project Title

Advance driver assistance system

Project Area of Specialization

Artificial Intelligence

Project Summary

Project summary:

ADAS: Advance Driver Assistance System

This is AI based solution to monitor drive in-cabin and out-cabin

In in-cabin mode driver will be monitored, his authentication (face recognition if the driver is authorized), and driver monitoring like if driver is drowsy/sleep or if he is smoking, of he has not tied seat belt, his head eye movement etc.

In Out-cabin mode, roads will be detected (like in which lane driver is driving) and if there is any vehicle or pedestrian comes under red zone area or near to the vehicle is will generate alarm, there will road lane detection, sign boards for drive also driver’s speed at what speed he is driving to check his performance etc.

Project Objectives

Objective is to monitor driver.This is for fleet management companies where driver authentication and assistance is required ,Also companies like uber and careem.  

Moreover the role of ADAS is to prevent deaths and injuries by reducing the number of car accidents

Essential safety-critical ADAS applications include:

• Pedestrian detection/avoidance

• Lane departure warning/correction

• Traffic sign recognition

• Automatic emergency alarm

Project Implementation Method

Method:

In-cabin:

Face detection

Drowsiness detection

Seat belt

Head motion

All will be done using image processing and machine learning (AI)

Camera will be mounted inside a car for in cabin and it will be connected with laptop

Live video will be processed.

Out-cabin:

Lane detection,

Vehicle and pedestrian detection

Output through voice

Camera will be installed outside a car

Camera will be connected with laptop

There will be region of interest in the video stream and if pedestrian or vehicle comes inside that region of interest (or near to the vehicle) it will generate an alarm

Dashboard will show overall results, history.

MORE FEATURES

• There is front end involved with AI modules incorporated with it

• There will be a dashboard developed (front end application) in flask (html, CSS, python) to visualize everything

• Real time detection of driver activities and send it to administrator.

• History maintaining with image, time, date

• There can be some research aspect as well, like data collection for/of drivers to train machine learning model for driver monitoring

Benefits of the Project

Benefits of ADAS are to keep driver in supervision (intended to monitor driver from a distance)

Safety majors are to avoid collisions by using technologies to alert drivers to potential hazards or take over control of the vehicle to avoid such danger.

Adaptive features. Automated lighting, adaptive cruise control, and pedestrian crash avoidance mitigation (PCAM) are features that incorporate navigational warnings to alert drivers to potential dangers, such as vehicles in blind spots, lane departures, and more.

Sensors have the potential to self-calibrate in the future to focus on the inherent safety and dependability of these systems.

Technical Details of Final Deliverable

Tools and Technology used

• Flask

• Cloud

• Python

• Cameras

• AI modules

• machine learning model

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com