# Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor
###### Abstract  
Pollutants degrade the performance of LiDAR sensors used in autonomous vehicles. Therefore, there is an urgent need to develop cleaning technology for these sensors. In this study, a solid-state LiDAR sensor was selected as a target and sprayed/dried with 2.5 g of a mixture of Arizona dust and Kaolin. To achieve optimal LiDAR cleaning performance, the washer pressure, spray time, spray angle, and target point were selected as major variables. Additionally, an optimal cleaning solution for each spray was formed via the design of experiments and optimization techniques. Model suitability was observed for the second spray through to the fourth. The cleaning rate increased with the washer pressure and spray time. The influence of these variables decreased as the number of sprays increased. The spray angle and target point exhibited no significant influence, but excellent cleaning was observed in some central areas. Verification test results were within 3% for the second through fourth sprays, indicating reliability. This study used a designed experiment with 30 scenarios to reveal optimized conditions for protecting the sensor performance from external visibility obstructions. Disclosing the optimization method lowers the barrier for sensor cleaning manufacturers to develop their own technology, which ultimately enhances safer and more efficient autonomous driving. Arizona dust; experiment design; Kaolin; LiDAR; optimization experiment; viscosity +
Footnote †: journal: Medical  
Several studies have been conducted to address the performance degradation of sensors used in autonomously driven vehicles that is caused by environmental pollutants, such as snow, dust, sand, and mud. Technologies to detect vehicles in inclement weather data sets [1], research on generative adversarial networks to remove raindrops from single images [2], and technologies to restore images through de-raining [3] exist. Various studies have focused on solving cognitive degradation caused by external environmental factors through image-filtering algorithms [4]. Additionally, a study was conducted on the recognition rate through dust synthesis in a virtual camera scenario [5]. Hyundai Motor Company is set to mass-produce camera sensor cleaning systems while developing their Rotator-Cam technology for level-4 autonomous driving [6]. XenomatiX has established a partnership with Kautex Clear Vision Systems to develop LiDAR-suitable cleaning solutions for adverse weather and cover contamination [7]. Furthermore, premium vehicles, such as the BMW 7 Series and Mercedes S-Class, which feature cleaning systems for some of their sensors, demonstrate the importance of maintaining sensor accuracy under all environmental conditions [8; 9]. Research on technologies to detect foreign substances on sensors [10] is urgently required along with the development of innovative solutions to ensure consistent and stable data collection for autonomous driving systems by optimizing sensor cleaning technology. This study developed an optimized cleaning technological design method for LiDAR sensors that use light [11] instead of radar with relatively high permeability. Among several sensors used in autonomous driving technology, LiDAR sensors that had been affected by external foreign substances that degrade their performance were selected through the design of experiments (DOE) method. Various types of LiDAR sensors were examined to select a target sensor, and a solid-state LiDAR sensor (manufacture: AutoL, Model name: G32) that is more highly evaluated than mechanical LiDAR sensors in terms of design was selected. Based on an existing study [12], a sample to reproduce strong dust was prepared by mixing Arizona dust (ARI) and Kaolin (KL) with 50% water. An amount of 2.5 g of the sample was sprayed and dried. The washer pressure, spray time, spray angle, and target point were selected as the main factors for cleaning optimization. A total of 30 standard experimental targets were selected through DOE.