Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.

1. AUTOMATED DRIVER FATIGUE
DETECTION AND ROAD ACCIDENT
PREVENTION SYSTEM
An Intelligent Approach to Solve a Fatal Problem

2. THE PROBLEM
At least 4,284 people, including 516 women and 539
children, were killed and 9,112 others were injured in 3,472
road accidents across Bangladesh in 2017.

3. THE SOLUTION
Some of those accidents could have been avoided if proper systems were
implemented at the time. This project focuses on creating a system based on EEG
(Electroencephalogram) and ECG (electrocardiogram) signal from driver which will
alert a driver about drowsiness while driving.

4. PROJECT OBJECTIVE
Primary objective of this project is to monitor a driver’s brain wave, heart rate to
calculate his physical & mental fitness to drive a vehicle. The system takes those vitals
as input then calculates the probability of the drivers physical and mental ability to
drive a car safely.

5. TOOLS & APPARATUS
• Raspberry Pi
• Portable EEG Headset
• Wristband Heart Rate Monitor
• Various Sensors

6. TECHNOLOGIES
• PHP
• JavaScript
• Python
• Machine Learning
• Gradient Boosting
• Hyper Optimizer
• Signal Processing

7. DATA COMMUNICATION
FIG [1]: DATA COMMUNICATION METHOD

8. SIGNAL PROCESSING
FIG [2]: RAW EEG DATA FIG [3]: TIME DIVISION MULTIPLEXED

9. PROCESSING METHODS
FIG [4]: MACHINE LEARNING METHODS

10. IMPLEMENTATION
FIG [5]: WEB VIEW FOR THE SYSTEM

11. PRELIMINARY RESULTS
The method mentioned above can detect driver fatigue from EEG with 75% accuracy.
With more test set data and continuous time series data can enhance the accuracy
rate greatly.
FIG [4]: BATCH TEST REPORT

12. FUTURE PLANS
• In future this system will be transform to an app base solution. At the time of
issuance of driving license every driver has to registrar in this app. By using this app
law enforcement agency can track vehicle position and driver’s condition.
• With further enhancement this system can detect obstacles like slow moving
vehicles, pedestrians, or pets which automatically slow down or stop the engine.

13. CONCLUSION
Most promising field of use for this system is public transportations and long route
transportations. It can also be used for further analysis of the driving pattern. People
can also use this system to properly monitor their vehicles for any wrong driving
pattern.
By:
Arman Hossain &
Shahinur Shamshad
CSE 11’th Batch,
SMUCT, Dhaka
Supervisor:
Tasnim Niger
Lecturer,
Dept. of CSE,
SMUCT, Dhaka