Traffic safety margin inference via machine learning for 3D spatial modeling

通过 3D 空间建模的机器学习进行交通安全裕度推断

• 批准号: 572807-2022
• 负责人: Fieguth, PaulPW
• 金额: $ 2.19万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765887
• 关键词: Traffic; safety; margin; inference; via

A great deal of road and traffic intersection safety is based on statistics of accidents, injuries, and fatalities, but which leads to the dreadful conclusion that a statistically significant number of accidents, injuries, and fatalities need to take place before changes and improvements can begin to be made.Surely it would be enormously preferable to observe intersections and to make note of close calls (near-accidents) which are far more common than actual accidents, but which do permit assessments of safety to be estimated. Although the human eye and visual system are fairly good at spotting near-accidents, it is unrealistic to place people, for days or weeks, at uncountable intersections around the country.Instead, many cities and municipalities have already installed video systems, built by Miovision Technologies Inc. of Kitchener, Ontario, which could provide the relevant data. Currently such data are used for counting vehicles of different types, as a function of date and time, for traffic planning purposes. However the large existing installation of these video systems offers an outstanding opportunity to provide a significant value-added in terms of safety assessment.The University of Waterloo is proposing to partner with Miovision to develop advanced methods of computer vision to measure the nearest distance as a vehicle passes a pedestrian or a bicyclist, to obtain data on close calls which far exceed the 364 cyclist and pedestrian deaths per year, creating a valuable product for Miovision, essential data for municipalities and planners, and ultimately improved quality of life for Canadians.

大量的道路和交通交叉口安全是基于事故、伤害和死亡的统计数据，但这导致了一个可怕的结论，即统计上大量的事故、伤害，在改变和改进开始之前，必须发生伤亡事故。当然，观察交叉路口并记录下千钧一发的时刻是非常可取的（准事故），这比实际事故更常见，但确实允许对安全性进行评估。尽管人眼和视觉系统在发现事故现场方面相当出色，但要让人们在全国无数的十字路口停留数天或数周是不现实的。相反，许多城市和市政当局已经安装了由Miovision Technologies Inc.制造的视频系统。的Kitchener，安大略，它可以提供相关数据。目前，这些数据用于计算不同类型的车辆，作为日期和时间的函数，用于交通规划目的。然而，这些视频系统的大量现有安装提供了一个绝佳的机会，在安全评估方面提供了显着的增值。滑铁卢大学提议与Miovision合作开发先进的计算机视觉方法，以测量车辆经过行人或骑自行车的人时的最近距离，获得远超过每年364名骑自行车者和行人死亡的近距离数据，为Miovision创造了一个有价值的产品，为市政当局和规划者提供了必要的数据，并最终改善了加拿大人的生活质量。