Abstract
Off-road intelligent vehicle is an important application about Internet of Vehicles technology used in the transportation field, and the front obstacle recognition method is the key technology for off-road intelligent vehicle. In this paper, based on smart data aggregation inspired paradigm of IoT applications, we mainly study perception technology in vehicle networking by using image data and one symmetrical speeded-up robust features detector (SURF). By considering symmetry and image data aggregation, we found that data aggregation had the ability of providing global information for Internet of Vehicles systems. After we have built the experiment platform, the experiment results showed that this method is faster than Scale-Invariant Feature Transform algorithm in this case, which can satisfy the water detection accuracy and the real-time requirement. So, this method is effective for the water images detection with great symmetry to off-road intelligent vehicle, and it also gives a useful reference about environment perception technology and smart data aggregation inspired paradigm used in future Internet of Vehicles, intelligent vehicle, and traffic safety applications.
Highlights
In recent years, the Internet of ings has led to the explosive development of data in almost each engineering field
Off-road intelligent vehicle is a part of Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS) systems. rough the delivery of different data, such as image data in IoV, environmental data in ITS and driver behavior data, and IoT smart data aggregation, the useful information that people want can be directly given back to IoV system. at is to say, the data transmission of off-road intelligent vehicles is an inspired paradigm of IoT applications [2, 28,29,30,31,32,33,34,35,36]
Off-road intelligent vehicle is an important topic of robotics in the field of Internet of Vehicles, safety driving assist system, and vehicle active safety
Summary
We mainly consider the symmetry and image data aggregation for off-road intelligent vehicle. With the rapid development of Internet of ings technology, especially the research and application of wireless sensor networks, Cooperative Vehicle Infrastructure System came into being. Integral image as an important conception of SURF algorithm is proposed by Viola and Jones [24, 25, 28], and it greatly improves the speed of feature point detection. In order to get an integral image, SURF employs a Hessian matrix approximation. Since an integral image is employed to approximate the second order Gaussian derivatives, SURF is more efficient to extract interest points. Hessian matrix H is expressed by using a function, including both space X (x, y) and scale σ based on the following formula [2, 28]: 2.4.
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