scholarly journals A Novel Signal Processing Based Driver Drowsiness Detection System

2021 ◽  
Vol 3 (3) ◽  
pp. 176-190
Author(s):  
Judy Simon ◽  
Aishwarya A ◽  
Mahalakshmi K ◽  
A Naveen Kumar
Keyword(s):  
Mental State ◽  
Traffic Accidents ◽  
Detection System ◽  
Research Work ◽  
Steering Wheel ◽  
Main Process ◽  
Brain Wave ◽  
Drowsiness Detection ◽  
Brain Signals ◽  
Attention Level

Drowsiness is a major cause of vehicle collisions and it most of the cases it may cause traffic accidents. This condition necessitates the need to develop a drowsiness detection system. Generally, the degree of sleep may be assessed by the number of eye blinks, yawning, gripping power on the steering wheel, and so on. These methods simply compute the actions of the driver. Henceforth, this research work proposes a Brain Computer Interface (BCI) technology to evaluate the mental state of brain by utilizing the EEG signals. Brain signal analysis is the main process involved in this project. Depending on the mental state of the drivers, the neurons pattern differs. Different electric brain signals will be produced in every neurons pattern. The attention level of brain signal varies from general state when the driver is sleeping mentally with eyes open. Various frequency and amplitude of EEG based brain signal are collected by using a brain wave sensor and the attention level is analyzed by using a level splitter section to which the brain signals are made into packets and transmitted through a medium. Level splitter section (LSS) figures out the driver’s state and provides a drowsiness alarm and retains the vehicle in a self-controlled mode until the driver wakes up. Additionally, this research work will provide an alert to the users and control the vehicle by employing the proposed model.

scholarly journals Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 943 ◽  
Author(s):  
Sadegh Arefnezhad ◽  
Sajjad Samiee ◽  
Arno Eichberger ◽  
Ali Nahvi
Keyword(s):  
Feature Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Steering Wheel ◽  
Support Vector ◽  
Inference System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Neuro Fuzzy

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

scholarly journals In-tunnel Accident Detection System based on the Learning of Accident Sound

2021 ◽  
Vol 15 (1) ◽  
pp. 81-92
Author(s):  
Linyang Yan ◽  
Sun-Woo Ko
Keyword(s):  
Neural Network ◽  
Detection Rate ◽  
Traffic Accidents ◽  
Deep Neural Network ◽  
Detection System ◽  
Recall Rate ◽  
Vehicle Accident ◽  
Special Environment ◽  
Accident Detection ◽  
Sound Features

Introduction: Traffic accidents are easy to occur in the tunnel due to its special environment, and the consequences are very serious. The existing vehicle accident detection system and CCTV system have the issues of low detection rate. Methods: A method of using Mel Frequency Cepstrum Coefficient (MFCC) to extract sound features and using a deep neural network (DNN) to learn sound features is proposed to distinguish accident sound from the non-accident sound. Results and Discussion: The experimental results show that the method can effectively classify accident sound and non-accident sound, and the recall rate can reach more than 78% by setting appropriate neural network parameters. Conclusion: The method proposed in this research can be used to detect tunnel accidents and consequently, accidents can be detected in time and avoid greater disasters.

Research on Intelligent Steering Wheel Technology Based on Multi Sensor

2014 ◽  
Vol 488-489 ◽  
pp. 1130-1133
Author(s):  
Yuan Bai ◽  
Xiao Dong Tan
Keyword(s):  
Traffic Safety ◽  
Automobile Industry ◽  
Traffic Accidents ◽  
Drunk Driving ◽  
Steering Wheel ◽  
Private Car ◽  
Driving Condition ◽  
Fatigue Driving

At present, the automobile industry is developing rapidly, the private car is widely popularized, and the hidden dangers of traffic safety exist. The phenomenon of drunk driving and fatigue driving becomes more and more serious, and the improvement for steering wheel could effectively prevent traffic accidents. This paper introduces and analyzes the intelligence of steering wheel in three major aspects, they respectively include intelligent grip detection, which tests if a driver is of fatigue driving; hart rate detection, which tests if a driver is in normal driving condition; alcohol detection, which tests if a driver drinks too much, and it predicts the possibility of accident from the drivers state, and timely gives out signal to warn the driver.

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