Nanocoatings Testing for Improved Perception in Adverse Weather Autonomous Driving

在恶劣天气条件下的良好感知是阻碍自动驾驶汽车广泛部署的最大技术障碍之一，特别是在北方气候。滑铁卢大学的Automoose自动驾驶汽车项目在过去三年里一直致力于研究自动驾驶汽车在恶劣天气下的自动感知能力，在消除天气影响的视觉和激光雷达处理领域处于领先地位。这家工业合作伙伴目前正在评估使用低能量涂料来改善自动驾驶车辆传感器(LiDAR和相机)在大雨/雪条件下的性能，以及驱赶鸟类粪便、盐环和昆虫等碎片。该项目将把炼金术涂层应用于Autonomoose测试车，并在恶劣天气下评估有无涂层的视觉和激光雷达处理算法的基准性能。该项目将在天气允许的情况下专注于雨雪条件，并可能导致加拿大冬季准备好的自动驾驶汽车的发布时间大幅缩短，并使炼金术在国际自动驾驶汽车传感器制造市场上占据中心地位。

Robust perception in harsh weather conditions is one of the biggest technical hurdles preventing the widespread deployment of autonomous vehicles, particularly in northern climates. The Autonomoose self-driving car program at the University of Waterloo has been working on autonomous perception capabilities for self-driving cars in adverse weather for the last three years, and is a leader in the areas of vision and lidar processing to remove the effects of weather. The industrial partner, Alchemy, is currently evaluating the use of low energy coatings to improve autonomous vehicle sensors (LiDAR and Camera) performance in heavy rain/snow conditions, as well as repelling debris such as bird droppings, salt rings, and insects. This project will apply the Alchemy coating to the Autonomoose test vehicle, and evaluate benchmark performance of vision and lidar processing algorithms with and without the coatings in adverse weather. The project will focus on both rain and snow conditions, weather permitting, and could lead to a significant reduction in the time to release of winter-ready self-driving cars in Canada, and to a central position for Alchemy in the international self-driving car sensor manufacturing market.