The aim of this study is to design and implement a system that detect driver sleepiness and warn driver in real-time using image processing and machine learning techniques. Viola-Jones detector was used for segmenting face and eye images from the cameraacquired driver video. Left and right eye images were combined into a single image. Thus, an image was obtained in minimum dimensions containing both eyes. Features of these images were extracted by using Gabor filters. These features were used to classifying images for open and closed eyes. Five machine learning algorithms were evaluated with four volunteer's eye image data set obtained from driving simulator. Nearest neighbor IBk algorithm has highest accuracy by 94.76% while J48 decision tree algorithm has fastest classification speed with 91.98% accuracy. J48 decision tree algorithm was recommended for real time running. PERCLOS the ratio of number of closed eyes in one minute period and CLOSDUR the duration of closed eyes were calculated. The driver is warned with the first level alarm when the PERCLOS value is 0.15 or above, and with second level alarm when it is 0.3 or above. In addition, when it is detected that the eyes remain closed for two seconds, the driver is also warned by the second level alarm regardless of the PERCLOS value. Designed and developed real-time application can able to detect driver sleepiness with 24 FPS image processing speed and 90% real time classification accuracy. Driver sleepiness were able to detect and driver was warned successfully in real time when sleepiness level of driver is achieved the defined threshold values.

Brain Computer Interfaces (BCI) are applications that allow users to communicate and control external devices directly by analyzing changes in brain activity without using muscle and nerve cells, which are normal pathways of the brain. It can also be said that BCIs are an alternative means of communication between the human brain and the outside world based on the electrical activities of brain activity, which can be measured by electroencephalography (EEG) devices. In the EEG measured from the human brain, when a person wants to move a limb, the potentials associated with the event are observed in the EEG. This suggests that information about changes in the activity of the human brain in the cognitive or movement decision process can be detected in the observed EEG.In this study, the attributes of the signals obtained using a four-channel EEG recorder are extracted and classified. Because the experimental study was performed while the user was awake, it processed beta signals. Considering the artifacts, the processed data was used as input data for the interface by realizing offline and online trial. The data obtained from the EEG device was processed in a computer and transmitted to a microcontroller used to control the model vehicle. Data communication is carried out wirelessly. The model vehicle is allowed to move forward-backward / right-left and diagonally.