Real Time Driver Alert Mechanism
The domain of "Real Time Driver Alert Mechanism" is Machine Learning. Machine Learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. Among many other issues of road accidents, drowsiness has become one of the major and the most cr
2025-06-28 16:28:54 - Adil Khan
Real Time Driver Alert Mechanism
Project Area of Specialization Computer ScienceProject SummaryThe domain of "Real Time Driver Alert Mechanism" is Machine Learning. Machine Learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. Among many other issues of road accidents, drowsiness has become one of the major and the most crucial issue which results in serious injuries and even cause deaths. To overcome this problem, an innovative system to avoid driver’s drowsiness is proposed which is based on image processing, IOT and ML to effectively detect drowsiness on real time and makes driver to stay alert/drive consciously. IOT based RTDDAM plays a vital role to save human lives and the most reliable, efficient and robust option to effectively handle these types of problem with higher accuracy which helps to prevent accidents. Our proposed methodology is based on both visual and non-visual parameters and both factors interact with each other using Master-Slave phenomenon. Ultimately, our objectives/features make our system more reliable, efficient as well as high responsive.
Project ObjectivesThe main objective our project "Real Time Driver Alert" Mechanism is to provide a solution for drowsiness to prevent accidents. The uniqueness of our project is the hybrid approach based on both visual and non-visual parameter and both factors interact with each other using Master-Slave phenomenon.
There are two types of approaches used to detect driver's fatigue that are ECG and EMG. SDLP without focusing on body. Various models are designed to alert the driver during fatigue state, still there are multiple loop holes to efficiently develop smart driver drowsiness mechanism. However, in this project we are targeting multiple input parameters such as human pulse, facial landmark detector (eye aspect ratio) is tremendously affected in fatigue condition.
Project Implementation Method
The hardware gadget comprised of pulse sensor, high resolution camera, raspberry pie 3 B+ with accessories, GSM module and Bluetooth module. However, camera is placed on the dash board in front of driving seat which will capture the facial landmarks and maintain brief driver fatigue state history. Moreover, pulse sensor is attached on the wrist along with Bluetooth module and controller. Furthermore, with the assistance of Bluetooth module (slave), the pulse sensor value receives by Raspberry pi module (master) which is attach to the facial landmarks detecting module. Both of these input parameters play an effective role to compute and provide fruitful results in real time.
Benefits of the ProjectThe benefits of the "Real Time driver Alert Mechanism" is as follow:
- This Project helps to reduce in road accidents by making alert to drivers.
- If any parameter of the system will damage, other will work accurately and efficiently.
- By using Pulse Sensor (BPM), we can detect the drowsiness of non-visual parameter.
- By using Pi- cam (EAR), we can detect the drowisness of visual parameter.
- Detect the drowsiness in real time accurately and efficiently.
The Technical Details of Final Deliverable of "Real Time Driver Alert Mechanism" :
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 64300 | |||
| Arduino Uno R3 | Equipment | 1 | 2500 | 2500 |
| Raspberry Pi 4 | Equipment | 1 | 35500 | 35500 |
| Raspberry Pi Camera 8mp | Equipment | 1 | 9000 | 9000 |
| Pulse sensor | Equipment | 1 | 1500 | 1500 |
| Jumper wire | Equipment | 10 | 10 | 100 |
| HC-05 Bluetooth Module | Equipment | 1 | 800 | 800 |
| Buzzer | Equipment | 1 | 150 | 150 |
| BreadBoard | Equipment | 1 | 250 | 250 |
| GPIO Expansion Board | Equipment | 1 | 750 | 750 |
| GPIO 40 Pins | Equipment | 1 | 450 | 450 |
| Memory Card 64gb | Equipment | 1 | 3000 | 3000 |
| Raspberry pi Case | Equipment | 1 | 1500 | 1500 |
| Arduino Case | Equipment | 1 | 350 | 350 |
| Pi Cam Stand | Equipment | 1 | 800 | 800 |
| HDMI Cable | Equipment | 1 | 1000 | 1000 |
| HDMI to VGA Converter | Equipment | 1 | 2000 | 2000 |
| VGA Cable | Equipment | 1 | 1000 | 1000 |
| C port Charger | Equipment | 1 | 1500 | 1500 |
| Mouse | Equipment | 1 | 650 | 650 |
| KeyBoard | Equipment | 1 | 1500 | 1500 |