Nanocoatings Testing for Improved Perception in Adverse Weather Autonomous Driving
纳米涂层测试可改善恶劣天气自动驾驶的感知
基本信息
- 批准号:538515-2019
- 负责人:
- 金额:$ 1.82万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Engage Grants Program
- 财政年份:2019
- 资助国家:加拿大
- 起止时间:2019-01-01 至 2020-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
在恶劣天气条件下的强大感知是阻碍自动驾驶汽车广泛部署的最大技术障碍之一,特别是在北方气候条件下。滑铁卢大学的Autonomoose自动驾驶汽车项目在过去的三年里一直致力于研究自动驾驶汽车在恶劣天气下的自主感知能力,并且是视觉和激光雷达处理领域的领导者,以消除天气的影响。 Alchemy的工业合作伙伴Alchemy目前正在评估使用低能量涂层来提高自动驾驶汽车传感器(LiDAR和摄像头)在大雨/大雪条件下的性能,以及排斥鸟粪、盐环和昆虫等碎片。该项目将在Autonomoose测试车辆上应用Alchemy涂层,并评估在恶劣天气下使用和不使用涂层的视觉和激光雷达处理算法的基准性能。 该项目将重点关注雨雪条件,天气允许的情况下,并可能导致在加拿大发布冬季准备自动驾驶汽车的时间大幅减少,并使Alchemy在国际自动驾驶汽车传感器制造市场中处于中心地位。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Waslander, Steven其他文献
Canadian Adverse Driving Conditions dataset
- DOI:
10.1177/0278364920979368 - 发表时间:
2021-04-01 - 期刊:
- 影响因子:9.2
- 作者:
Pitropov, Matthew;Garcia, Danson Evan;Waslander, Steven - 通讯作者:
Waslander, Steven
Waslander, Steven的其他文献
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{{ truncateString('Waslander, Steven', 18)}}的其他基金
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
RGPIN-2019-05939 - 财政年份:2022
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
DGDND-2019-05939 - 财政年份:2021
- 资助金额:
$ 1.82万 - 项目类别:
DND/NSERC Discovery Grant Supplement
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
RGPIN-2019-05939 - 财政年份:2021
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
DGDND-2019-05939 - 财政年份:2020
- 资助金额:
$ 1.82万 - 项目类别:
DND/NSERC Discovery Grant Supplement
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
RGPIN-2019-05939 - 财政年份:2020
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
RGPIN-2019-05939 - 财政年份:2019
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Active Robotic Vision: Dynamic Sensor Systems for Informed Robotic Perception
主动机器人视觉:用于知情机器人感知的动态传感器系统
- 批准号:
DGDND-2019-05939 - 财政年份:2019
- 资助金额:
$ 1.82万 - 项目类别:
DND/NSERC Discovery Grant Supplement
Quadrotor perception and planning in confined spaces
四旋翼飞行器在有限空间中的感知和规划
- 批准号:
484724-2015 - 财政年份:2018
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
Autonomous Aerial Vehicle Navigation without Reliance on GPS
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RGPIN-2014-03915 - 财政年份:2018
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Quadrotor perception and planning in confined spaces
四旋翼飞行器在有限空间中的感知和规划
- 批准号:
484724-2015 - 财政年份:2017
- 资助金额:
$ 1.82万 - 项目类别:
Collaborative Research and Development Grants
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