# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 1 Introduction  
In recent times, interest in autonomous driving has increased. Technology that recognizes the surrounding environment is the core technology of autonomous driving. Recognition technology recognizes the surrounding environment using cameras, LiDAR, and radar. Object detection and tracking are very important processes for recognizing the surrounding environment and maintaining control in autonomous driving. These technologies are directly related to the development of sensor technology. A camera is the most popular sensor for recognizing surroundings. It is relatively easy to implement software that can detect static and dynamic obstacles within a camera. Such a camera can also provide high-resolution images. By means of these features, objects such as road signs, traffic lights, lanes, and obstacles can be recognized, and multiple objects can be recognized concurrently [1; 2; 3; 4]. The disadvantage of a camera is that it is difficult to extract distance information from camera images. In order to overcome this disadvantage, many studies have focused on producing stereo cameras and developing algorithms to extract distance information [5; 6; 7]. Furthermore, owing to the error ratio of distance based on the unique characteristics of the camera, it is generally difficult to ensure consistent performance in varied environments [8; 9]. A LiDAR sensor can replace a camera as recognition technology. LiDAR sensors emit a laser and measure distance by calculating the time it takes for the laser to reflect off an object and return. LiDAR sensors are classified into one-dimensional, two-dimensional, and three-dimensional based on the number of beams [10]. Three-dimensional LiDAR sensors are one of the most popular recognition sensors in the field of autonomous driving. These sensors have several advantages: they canmeasure distances precisely, have a long range, and can be used in dark field environments. A 3D LiDAR sensor can have better performance than a camera in collecting the shape and posture of an object using a point cloud, making it better at object recognition [11]. Radar emits electromagnetic waves and receives scattered or reflected waves generated from objects. Based on the Doppler effect, using changes in the frequency, the relative speed and relative position of the detected obstacle are determined [12]. Radar is not affected by bad weather conditions and has the advantage of being independent of light intensity [13]. However, it has a disadvantage in that it is difficult to accurately detect metal objects such as road signs, guardrails, and static objects using radar. Thus, a technology that maximizes the advantages of individual sensors through sensor fusion and improves the performance reliability of object recognition for the surrounding environment is essential. Approaches to improve the reliability of recognition technology for obstacles and surrounding environments using fusion LiDAR sensors and cameras are being extensively researched. Given that a context-awareness system needs multiple cameras, LiDAR sensors, and radar, the technology to achieve fusion between their respective data is also needed. Multiple LiDAR must be installed in the moving vehicle in order to recognize the omnidirectional environment. As autonomous vehicles require five or more LiDAR sensors, a fusion system that can control the data of multiple LiDAR sensors is needed. Such a fusion system must consider various technical elements such as synchronization among sensors, the processing time of input data, and the accuracy of object recognition. We propose a software architecture for a fusion system that connects multiple LiDAR sensors. First, we describe a CPU scheduler to efficiently manage the processes of multiple clients that receive data from multiple LiDARs. Second, we propose a multithreading method for efficient data processing on a server. Third, we propose the idea of inter-processing communication between client and server.