Nanocoatings Testing for Improved Perception in Adverse Weather Autonomous Driving

恶劣天气条件下的强大感知能力是阻碍自动驾驶汽车广泛部署的最大技术障碍之一，尤其是在北方气候条件下。滑铁卢大学（University of Waterloo）的autonomose自动驾驶汽车项目在过去三年中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，并且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。工业合作伙伴Alchemy目前正在评估低能耗涂层的使用，以改善自动驾驶汽车传感器（LiDAR和摄像头）在大雨/雪条件下的性能，以及排斥鸟粪、盐环和昆虫等碎片。该项目将在自动驾驶测试车上应用Alchemy涂层，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。在天气允许的情况下，该项目将专注于雨雪条件，并可能大大缩短在加拿大发布冬季准备的自动驾驶汽车的时间，并使Alchemy在国际自动驾驶汽车传感器制造市场占据中心地位。

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.