Iloshini, P.A.A.Thambawita, D.R.V.L.B.2022-01-262022-01-2620159789550481088http://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/8236/04-CST-Android%20mobile%20driving%20assistant%20for%20highway%20drivers%20.pdf?sequence=1&isAllowed=yMobile based driving assistant that always communicate with the driver in an interactive way, has not introduced yet. It assists the driver when he is driving on highways and driver can control the assistant through his voice command. Mobile Driving Assistant helps driver to clarify the way that he drives. In addition, the driving assistant carefully examines the facial expressions of the driver and if he is in a drowsy condition, assistant suggests alternatives to overcome from those situations. This is a friendly interactive guide for the highway drivers. Recalling the history, in 2002 Ji and Yang (2002) has presented a detection drowsiness system based on infrared light illumination and stereo vision. This system localizes the eye position using image differences based on the bright pupil effect. Brandt et al. (2004) has shown a system that monitors the driver fatigue and inattention. For this task, he has used VJ method to detect the driver’s face. Using the optical flow algorithm over eyes and head this system is able to compute the driver state. Tian and Qin (2005) have built a system for verifying the driver’s eye state. Their system uses Cb and Cr components of the YcbCr color space; with vertical projection function this system localizes the face region and with horizontal projection function it localizes the eye region. Once the eyes are localized the system computes eye state using a complexity function. Pallavi M, S. Gawali in 2012 their research paper demonstrated the new non-intrusive approach for monitoring driver drowsiness depending on the driver and driving data fusion. They use percentage of eye closure (PERCLOS) model for estimating driver status. The driving information such as lateral position and steering wheel angle also use for drowsiness detection. Multilayer perceptron neural network has been trained for optimal performance score in this research paper. Yong Du, Peijun Ma in 2008 published a research paper on effective vision based driver fatigue detection method. In this at primary stage, the inter-frame difference approach binding color information is used to detect face. Marco Javier Flores and Jose Maria Armingol in 2008 presented the basic model for drowsiness detection. For this they used Viola & Jones (VJ) method to detect the driver’s face. Once face is detected SVM is used to detect eye status from trained data. Methodology For implementation of the Mobile Driving Assistant application, Samsung Galaxy Core was used as the mobile device and the android version 4.1(API level 16) was used as the development environment. The internet and GPS services need to be activated in Mobile Device. The Mobile Driving Assistant is based on android platform supported mobile phones only. Java was used as programming language and common programming language to develop android applications. ADT bundle was handled as IDE for the implementation. Android voice recognition and android Text-To-Speech facilities were focused in order to maintain the voice discussion between the driver and the mobile driving assistant. Applications that available in Android platform can potentially make use of any speech recognition service on the device that's registered to receive a Recognizer Intent. Google's Voice Search application, which is pre-installed on many Android devices, responds to a Recognizer Intent by displaying the "Speak now" dialog and streaming audio to Google's servers. The Android platform includes a Text-to-Speech (TTS) capability. Also known as "speech synthesis", TTS enables an Android device to "speak" text in various languages. Face and eye blinking detection is the most important module of the mobile driving assistant. Haarcascade_lefteye_2splits.xml files, distributed with OpenCV package were used to detect eyes when eyes are opened. OpenCV 2.4.9.0 was used for the image processing purposes.enAutomobileAutomationScience and TechnologySystemComputer ScienceOther