Drowsy Driver Detection System Using Matlab Code
Drowsy Driver Detection System Using Matlab Code ->>->>->> https://urlgoal.com/2t9Zgz
Abstract:The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.Keywords: driver drowsiness; hybrid sensing; machine learning; physiological signal
Abstract:The driver drowsiness detection system using wearable EEG based on convolution neural network is presented. The EEG collection module, EEG signal processing module and early warning module formed a complete system which can be used in vehicle driving safety. The final experimental results show the great performance of the proposed method in vehicle driver drowsiness detection. Specifically, the equipment provides excellent classification efficiency, and the accuracy can reach 95.59% based on a one second time window samples using neural network with Inception module and reach 94.68% using modified AlexNet network module during simulation and tests. The proposed early warning strategy is also very effective.
Conclusions:The experimental results show the feasibility of the above method in vehicle driver drowsiness detection. The future work will focus on how to improve the system performance. The current EEG collection module will be replaced by a more accurate and wearable EEG collection module. This research will also continue to verify the effectiveness of the proposed method using a real environment in the future.
Vehicle driver drowsy driving can cause serious traffic accidents. In this study, a novel vehicle driver drowsiness detection method using electroencephalographic (EEG) signals based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety. 827ec27edc