Cameras allow for highly accurate identification of targets. However, it is difficult to obtain spatial position and velocity information about a target by relying solely on images. The millimeter-wave radar (MMW radar) sensor itself easily acquires spatial position and velocity information of the target but cannot identify the shape of the target. MMW radar and camera, as two sensors with complementary strengths, have been heavily researched in intelligent transportation. This article examines and reviews domestic and international research techniques for the definition, process, and data correlation of MMW radar and camera fusion. This article describes the structure and hierarchy of MMW radar and camera fusion, it also presents its fusion process, including spatio-temporal alignment, sensor calibration, and data information correlation methods. The data fusion algorithms from MMW radar and camera are described separately from traditional fusion algorithms and deep learning based algorithms, and their advantages and disadvantages are briefly evaluated.
Tunnels account for an increasing proportion of highways. Due to the semi-closed structure of tunnels, signal communication is difficult in tunnels. This review analyzes the signal data transmission requirements of intelligent network management systems, such as lighting systems, wind protection systems, fire protection systems, and vehicle and pedestrian positioning systems in tunnels. The selection of signal coverage and transmission methods are also discussed. The advantages and disadvantages of various networking methods are analyzed. This paper summarizes the wireless signal transmission, wired signal transmission and signal transmission modes of different tunnel types.
With the rapid development of intelligent transportation, it is of great significance to improve road traffic safety and traffic efficiency by tracking road users in real time. In this paper, a roadside multi-source data acquisition platform is designed based on two main sensors, lidar and camera, to perform time synchronization between lidar data and camera data. The point cloud data and image data of the same frame are the research objects. Based on Zhang Zhengyou’s calibration method and the plane target model theory, the camera internal parameters and the joint external parameters of the lidar and camera are respectively calibrated to obtain the internal and external parameter matrix. The error is 0.09 pixels, and the external parameter calibration error is 0.22 pixels. The registration test of lidar and camera under different roadside layout conditions is designed to verify the adaptability of the registration method. When the camera is arranged and installed, the distance between the horizontal and vertical directions should not exceed 90 cm.
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