# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
## 7 Conclusions  
The main goal of this study was to use the Kalman filter to improve the application of automotive LiDAR signals in severe weather. At present, LiDAR is used in many places, including in autonomous vehicles, but when the weather is bad, they are impossible to operate. More and more studies focus on how to improve LiDAR data in adverse weather. This paper used a combination of two methods to filter out undesired noise, first by using a nearest neighbor segmentation algorithm, and second by Kalman filtering. Both methods can be used separately, but they were combined here to filter out more noise. The nearest neighbor point cloud segmentation algorithm was used to filter out the noise of rain, and the Kalman filter was used to filter out the noise of dense smoke. The results of this study showed that signals measured in adverse weather can be restored to near normal data. The method described in this study can be used to obtain improvement, using the raw data for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison of** & **Adverse Weather** & & \\
**Normal Weather and** & **and Used** & **\(x\)-axis** & **\(y\)-axis** \\
**Adverse Weather.** & **Algorithms** & & \\ \hline Rainy weather & Rain & 0.0164 & 0.0134 \\  & Nearest neighbor & 0.0132 & 0.0103 \\  & segmentation & 0.0109 & 0.0087 \\  & Smoky weather & 0.0750 & 0.0849 \\ Smoky weather & Nearest neighbor & & \\  & segmentation & 0.0743 & 0.0841 \\  & algorithm & & \\  & Kalman filter & 0.0634 & 0.0646 \\  & Rainy weather and & & \\ Rainy weather and & smoky weather & & \\ smoky weather & Nearest neighbor & & \\  & segmentation & 0.0198 & 0.0312 \\  & algorithm & & \\  & Kalman filter & 0.0166 & 0.0277 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of RMSE between simulated normal weather and adverse weather before and after improvement (unit: meters). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Rainy** & **Smoky Weather** & **Rainy Weather and** \\  & & & **Smoky Weather** \\ \hline \(x\)-axis & 33.5\% & 15.5\% & 11.5\% \\ \(y\)-axis & 35.1\% & 23.9\% & 16.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Improvement rate after correction under three severe weather conditions. comparison of normal weather with the bad weather data. The main objectives completed in this study are as follows:  
1. This paper combined two algorithms to restore normal weather data more effectively. 2. Three simulated weather patterns were measured, and the LiDAR response observed. 3. The improvement rate obtained from the combination of the two algorithms is between 10% and 30%. This research describes a LiDAR system capable of reducing environmental noise, which includes an optical radar and a processing module. The LiDAR obtains point cloud model data, and a processing module processes it from the self-detection environment. The processing module includes a nearest neighbor point cloud segmentation algorithm and a Kalman filtering unit. The nearest neighbor point cloud segmentation algorithm receives and processes the point cloud model data to output filtered model data, and the Kalman filtering unit receives the filtered model data to filter and output low-noise environment model data. Looking to the future, the current measurements occurred in a controlled laboratory environment, and whether the system is controllable in real severe weather environments needs to be discussed via the following scenarios:  
1. Use scientific instruments to measure severe weather data, raindrop size, and smoke concentration and particle size, for quantification of severe weather values. 2. Add different kinds of severe weather for testing and verification. 3. Conduct actual road tests in different adverse weather conditions. 4. All equipment used in this is vehicle-specific. Although they were tested in the laboratory, they can be installed on vehicles in the future. If the above items can be thoroughly investigated, actual vehicle testing and verification will be carried out.