# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
###### Abstract  
A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in  
[PERSON] et al. [8; 9] proposed a plan for the development of LiDAR in 2016, comparing two different LiDARs (Ibeo Lux and Velodyne PUCK) in 2018. They evaluated the performance of the LiDAR in a fog chamber under stable foggy conditions. They found the performance to be affected, leading to a reduction in the visibility range under adverse weather conditions. [PERSON] et al. [10] proposed a 2D LiDAR algorithm for the detection and tracking of pedestrians. They studied pedestrian tracking and proposed a multi-person tracking algorithm based on the incorporation of a Kalman filter. [PERSON] et al. [11; 12] proposed using a convolutional neural network (CNN)-based LiDAR system to filter out point cloud noise in bad weather. They observed that in heavy rain or thick smoke, water droplets may be misinterpreted as an object in front of the vehicle, causing the vehicle to stop. They focused on a 2D input layer method that usually uses a birds-eye view (BEV) or image projection view. The PointPillars introduced in this method are based on the use of a feature extraction network to generate a pseudo image from the point cloud, which is then used as input for the backbone of the CNN. This method outperformed the KITTI's dataset object detection in terms of detection performance and reasoning time. They proposed a 2D method inspired by LiLaNet's CNN architecture. [PERSON] et al. [13] introduced and discussed the incorporation of full-waveform pixel and image acquisition and processing that would allow the LiDAR to penetrate the obscuring medium and reconstruct a 3D map of the surface. [PERSON] et al. [14] tested and verified automotive point cloud sensors under severe weather conditions. They documented both indoor and outdoor conditions and tested how fog and snow would affect the performance of the LiDAR. They found the accuracy to also be reduced by exposure to environmental noise [15]. There are numerous examples of the impact of adverse weather conditions on LiDAR worldwide [16]. Consequently, there has been much research on how to improve LiDAR data [17; 18]. Calibrated weather sensors have been used to measure the following relevant parameters: (1) transmission measurement of visibility in the range of 5 to 1000 m; (2) particle measurement of the particle size distribution of thick smoke from 0.4 to 40 microns; (3) rainfall intensity in the range of 0.001 to 1200 mm/h, as measured by a rain gauge and spectrometer. There have also been some studies on the efficacy of LiDARs of different wavelengths under different smoke densities. The same behavior is demonstrated at wavelengths from 350 to 1000 nm, regardless of the density or type of smoke, however, above 1000 nm, differences may appear. Thick smoke, especially convection fog, has an approximately 10% effect in the near-infrared range (1000-2400 nm). The wavelength is higher than the visible wavelength (400-800 nm) [19], so it is found that severe weather has some degree of influence on LiDAR [20]. In this current study, we first use LiDAR to make measurements under normal weather conditions and then simulate adverse weather conditions for rain, heavy smoke, and rain and heavy smoke.