Abstract
The goal of this study is to develop an internet of vehicles system with augmented reality technology. The system deals mainly with three subjects, namely, lane departure warning, forward collision detection and warning, and internet of vehicles. First, to deal with the subject of lane departure warning, the Hough transform is used in this study to extract the possible positions of lane lines from the region of interest of an image. The Kalman filter is further employed to remove noises and estimate the actual positions of car lane lines. The lane departure decision is then used to determine whether a lane departure situation occurs. Second, the Sobel edge detector and taillight detection method are used to locate the hypothetical region of the vehicle. The characteristic parameters within the hypothetical region can also be obtained through the Harris corner detection method. To verify the hypothetical region and identify the vehicle, the support vector machine algorithm is used. The collision decision is then applied to determine whether the distance between two vehicles is short, thus fulfilling the goal of forward collision detection and warning. In addition, a secure and easy-to-use internet of vehicles is achieved with the use of the Rivest–Shamir–Adleman encryption algorithm, which uses public and secret keys to encrypt and decrypt messages to achieve the task of user identification. Upon obtaining control of the vehicle, the driver has full access to the most up-to-date information provided by the driver assistance system. Finally, internet of vehicles applications incorporating the previously mentioned methods, smart glasses, and augmented reality are implemented in this study. Smart glasses provide the drivers easy access to information about the vehicle and warnings, which helps enhance driver convenience and safety considerably.
Highlights
In recent years, industrial giants, such as Facebook, Microsoft, Samsung, and Google, have launched a new revolution in visual technology
Experimental results of support vector machine (SVM) front vehicle identification and collision avoidance warning In this study, the front vehicle ID is based on the use of LIBSVM25 open source machine learning library proposed by C
All of the images are captured by Harris corner detection to obtain the eigenvector, and the training samples are normalized to 50 Â 45 pixels
Summary
Industrial giants, such as Facebook, Microsoft, Samsung, and Google, have launched a new revolution in visual technology. In 2016, the major bright spot is the expansion of the augmented and virtual realities of science and technology. The numbers of participants using devices and the developers of software in this revolution are increasing. Augmented reality (AR) is the combination of computer and real-world information; users can obtain relevant information at the right place and time. Virtual reality (VR) is the ideal artificial environment on a computer wherein a virtual environment is created in a seemingly real or physical manner. In the VR, the operator can interact with the controller in the virtual environment. In the Department of Electrical Engineering, National Chin-Yi University of Technology, Taiping Dist, Taichung, Taiwan
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