Pelation REBO: Developing a machine learning approach to automatically identify cycling near misses and root causes from dashcam footage

pelation是一家循环技术公司，旨在通过创新的设计和工程消除可持续移动障碍。Pelation目前的产品REBO专注于消除危险的近距离传球和近距离传球，它是一个连接互联网的自行车灯、仪表盘摄像头和启用gps的书签按钮，骑自行车的人只需点击一个按钮，就可以捕捉到以前无法获得的近距离传球镜头和信息。目前市场上的自行车道路数据给出了问题区域的概述，但需要对具体事件进行信心飞跃式推断和假设。如果没有事件背景，很难获得对事件根本原因的信心——我们的视频片段捕捉了这些缺失的信息，从而可以确定“未遂事故”是如何发展的。技术路线图的下一阶段是开发利用我们设备的视频片段和数据自动识别和分析自行车近距离脱靶的能力。这样做的目的是为这些“险些失误”提供可操作的见解，以确定其根本原因和潜在的修复方法。这将使地方当局能够更容易地理解、优先考虑和更快地实施行动计划，并产生更大的影响和物有所值。为了实现这一目标，Pelation项目的第一阶段是一项可行性研究，旨在研究关键的近脱靶影响因素，开发机器学习方法，并确定其他传感器规格，以便从我们的设备的素材和传感器中自动确定近脱靶和根本原因识别过程。Pelation将与牛津郡议会在这个项目上密切合作，确定他们的需求和要求，并核实当前的挑战领域。我们的新方法使用机器学习来自动识别骑自行车者提交的镜头中的近靶，这将大大减少分析这些现有数据集的时间和成本，并提供近靶根本原因的客观概述，从而提供立即可用和可操作的信息。该项目开发了一种创新的机器学习模型，用于从循环镜头中识别类别和关键因素，并开发了一种模式匹配算法，将关键事件因素与地理空间/运动学传感器数据相匹配。该项目的第二阶段将是开发可扩展的近靶识别和根本原因确定软件，该软件将内置到我们的设备和云分析平台中。随后将与当地政府合作进行大规模的道路试验（涉及不同的人员、地点和时间），以展示该技术在现实生活操作条件下的实用性。

Public descriptionPelation is a cycle technology company that aims to eliminate sustainable mobility barriers through innovative design and engineering. Focused on the elimination of dangerous near misses and close passes, Pelation's current product REBO, is an internet connected bike light, dashcam and GPS-enabled bookmark button that allows cyclists to capture previously unavailable near miss footage and information with a click of a button.Current cycling road data on the market give an overview of problem areas but require leap-of-faith inference and assumption on specific incidents. It is difficult to gain confidence in incident root causes without the incident context - our video footage captures this missing information which enables the determination of how near misses develop.The next stage of the technology roadmap is to develop capabilities to automatically identify and analyse cycling near misses using our device's video footage and data. This aims to produce, for these near misses, actionable insights to determine their root causes and potential fixes. This will allow local authorities to easily understand, prioritise, and implement action plans faster and with more impact and value for money.To achieve this, Phase One of Pelation's project is a feasibility study that sets out to research key near miss contributing factors, develop the machine learning approach, and identify additional sensor specifications required to automate the near miss determination and root cause identification process from our devices' footage and sensors. Pelation will be working closely with Oxfordshire County Council on this project - identifying their needs and requirements and verifying current challenge areas.Our novel approach using machine learning to automatically identify near misses from cyclists submitted footage will enable drastically reduced time and cost spent analysing these existing data sets and provide an objective overview of near miss root causes that provide immediately usable and actionable information. This project develops an innovative machine learning model to identify categories and key factors for near misses from cycle footage, and develops a pattern matching algorithm that matches key incident factors to geospatial/kinematic sensors data.Phase Two of the project will be to develop a scalable near miss identification and root cause determination software that will be built into our devices and cloud analysis platform. This will be followed by a large scale road trial (with a variety of people, places, and time) in collaboration with a local authority to demonstrate the usefulness of the technology in real life operating conditions.