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

公共自行车是一家自行车技术公司，旨在通过创新的设计和工程来消除可持续的移动障碍。Pelation目前的产品REBO专注于消除危险的近距离失误和近距离传球，是一款互联网连接的自行车灯，仪表盘摄像头和GPS功能的书签按钮，允许骑自行车的人点击按钮捕捉以前不可用的近距离失误镜头和信息。目前市场上的自行车道路数据给出了问题区域的概述，但需要对特定事件进行跨信仰的推断和假设。如果没有事故的背景，很难对事故的根本原因有信心-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的发展程度。技术路线图的下一阶段是开发使用我们设备的视频片段和数据自动识别和分析骑自行车未遂事故的功能。这旨在为这些未遂事件提供可操作的见解，以确定其根本原因和潜在的解决方案。这将使地方当局能够轻松理解、优先考虑和更快地实施行动计划，并产生更大的影响和更高的资金价值。为了实现这一目标，Pelation项目的第一阶段是可行性研究，旨在研究关键的未遂事件影响因素，开发机器学习方法，并从我们设备的镜头中确定自动化未遂事件确定和根本原因识别过程所需的其他传感器规格，传感器. Pelation将与牛津郡理事会就该项目密切合作，确定他们的需求和要求，并验证当前的挑战领域。我们使用机器学习的新颖方法自动识别骑车人提交的镜头中的未遂事件，这将大大减少分析这些现有数据集所花费的时间和成本，并提供对未遂事件根本原因的客观概述，从而提供立即可用和可操作的信息。该项目开发了一个创新的机器学习模型，用于从循环镜头中识别未遂事件的类别和关键因素，并开发了一种模式匹配算法，将关键事件因素与地理空间/运动传感器数据相匹配。该项目的第二阶段将开发可扩展的未遂事件识别和根本原因确定软件，该软件将内置于我们的设备和云分析平台中。随后将与地方当局合作进行大规模道路试验（有各种人员、地点和时间），以证明该技术在真实的生活操作条件下的有用性。

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.