Automated winter road surface condition monitoring system

雪灾期间和雪灾后的冬季路面状况监测对于加拿大负责冬季道路维护的大多数运输机构来说至关重要。 有关路面情况的资料可用以评估维修服务的需要、比较不同处理方法的成效，以及评估承办商在不同维修场地提供维修服务的质素。 路面状况的实时信息对于道路使用者来说也是非常宝贵的，他们可以使用这些信息来改善他们的出行和驾驶决策，例如在哪里，何时以及以什么模式出行。 目前，冬季路面状况的监测大多是通过个人观察和人工记录来完成的，这在可重复性，细节和及时性方面受到限制。 虽然传感器技术的最新发展，如连续摩擦测量设备（CFME），基于网络的监控视频和光谱冰雪覆盖传感器，为快速客观地评估路面状况提供了新的机会，但它们的实施成本很高，空间覆盖范围和完整性有限。 该项目旨在进一步推进我们新的冬季路况监测解决方案，该解决方案将机器视觉、人工智能和数据融合技术创新应用于云无线和互联网技术平台。 原型解决方案包括一个全自动数据收集单元，包括GPS，摄像头，红外温度计和其他传感器的接口，如犁/盐渍状态。 数据在车上被处理，然后被发送到中央服务器，在中央服务器处，来自大量参与车辆的数据被处理以生成具有高空间、时间和横向覆盖的路面状况信息。 这些完整信息的可用性有可能深刻改变冬季维护实践，并将使所有加拿大人受益，改善流动性，道路安全和环境保护。

Monitoring of winter road surface conditions during and after a snow storm is essential for most transportation agencies in Canada who are responsible for winter road maintenance. Information on road surface conditions can be used to assess the need for maintenance service, compare the effectiveness of different treatment methods, and evaluate the quality of the maintenance services delivered by contractors across different maintenance yards. Real-time information on road surface conditions is also invaluable to the road users who can use the information to improve their travel and driving decisions such as where, when and in what mode to travel. Currently, monitoring of winter road surface conditions is mostly done through personal observations and manual recording, which is limited in repeatability, details and timeliness. While recent developments in sensor technologies such as continuous friction measurement equipment (CFME), web-based surveillance video, and spectroscopic snow and ice cover sensors have afforded new opportunities for quick and objective assessment of road surface conditions, they are costly for implementation and limited in spatial coverage and completeness. The proposed project is to further advance our new winter road condition monitoring solution featuring innovative applications of machine vision, artificial intelligence, and data fusion techniques on a platform of cloud-based wireless and Internet technologies. The prototype solution includes a fully automated data collection unit consisting of a GPS, a camera, an IR thermometer, and interfaces to other sensors such as plowing/salting status. The data are processed onboard and then transmit to a central server where data from a large number of participating vehicles is processed to generate road surface condition information that is of high spatial, temporal and lateral coverage. The availability of such complete information has the potential to profoundly change the winter maintenance practice and will benefit all Canadians with improved mobility, road safety, and environmental protection.