Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles

Abstract

Unmanned technology is an important development project of today’s cutting-edge science and technology, which has a significant impact on social and economic development, national defense construction, and scientific and technological development. The rapid development of industrial information technology has driven the unmanned intelligent vehicle system to innovate and gradually enter the public’s view, and at the same time, the driving safety of unmanned intelligent vehicles is also widely concerned. Target information perception system is the foundation of unmanned system and the fundamental guarantee of safety and intelligence of unmanned vehicles. There are three key problems of target recognition in unmanned driving, namely, target classification, localization, and attitude determination. In the implementation of a networked virtual environment system, a flexible and complete perception model is needed as the guiding model of the system. Since 3D point cloud data can provide more spatial information than 2D RGB image data, it is more beneficial to determine the target category, position, and pose in 3D. In this paper, based on the existing research of unmanned intelligent vehicle perception system, we combine the application of fusion of 3D target information perception model and develop a nighttime unmanned system based on multiview fusion of 3D target information perception model. This system can simultaneously perform the detection of multiple categories of objects and predict the center point, length, width, height, and orientation of the objects, so that the unmanned car can sense the location of the surrounding objects when driving in the actual scene.