Pothole Identification and Management Autonomous System

背景：ICM Research 2016年为KwikFit进行的一项调查估计，英国驾车者每年因坑害造成的总损失为6.84亿英镑。沥青工业联盟(Alarm 2016)的研究发现，2015/2016年度向英格兰和威尔士地方当局索赔的总成本为2840万GB-其中76%直接归因于坑坑洼洼。虽然地方当局有处理道路损坏的法律责任，但它们在很大程度上依赖于公众向其报告的问题，以便进行评估和修复。坑洞报告、评估和修复的反应性质是低效的，而且在很大程度上是无效的。目的和目标：该项目的目的是开发技术，改进地方当局识别和管理坑洞的方式，旨在使他们能够改善道路并降低成本。主要目标是：1.开发负担得起的传感器技术，使车载传感器能够以高达65公里/小时的速度行驶，以识别和捕捉道路路缘和中心线之间的道路损坏。对道路损坏情况进行分类，以便报告。以提供受损情况的图像。以提供道路损坏的准确位置。建立典型损伤参数的“学习数据库”。以存储转发或实时方式报告道路损坏情况。在基于云的服务上捕获和存储数据。对数据进行映射，使其易于理解。该项目最初将侧重于交付一个原型，用于识别、分类、报告和共享有关坑洞的信息。在初步能力成功演示后，将开发实时应用程序。应用程序和好处：该项目将提供车载传感器，可以识别坑洞和其他道路损坏并对其进行分类，为道路损坏提供准确的位置，通过转发到云存储来报告发生的情况，并使数据能够通过网络浏览器访问，在地图上显示数据。我们认为，为地方当局道路提供全面地图的最有效机制将是在避难所和回收废物收集车上安装传感器。这可以由独立的调查工具来补充。结合地方当局的数据将提供一个地区和潜在的英国的全面地图。好处包括：1.为高速公路机构、地方当局和私人用户创建一个详细的基于网络的道路坑洞测绘来源。这些数据将包括损伤分类、成像和准确定位。这将使公路当局和地方当局能够在知情的情况下就道路维修的优先次序作出决定，并为私人订户提供一种工具，以避免对私家车造成道路损害。为有简易爆炸装置(IED)攻击风险的作战环境中的军事用户实时捕获和报告地面干扰的数据；使潜在威胁得以缓解。对无人精准农用车辆的自然和人为危害进行实时数据捕获和报告；能够在撞击之前采取避让行动。

Context:A 2016 survey for KwikFit by ICM Research estimated the annual total cost of pothole damage to UK motorists was £684m. Research by the Asphalt Industries Alliance (ALARM 2016) identified that the total cost of compensation claims against local authorities in England and Wales for 2015/2016 was £28.4m - 76% of which was directly attributable to potholes. Whilst local authorities have a legal responsibility for dealing with damage to roads, they are heavily dependent on issues being reported to them by the general public to enable assessment and repair. The reactive nature of pothole reporting, assessment and repair is inefficient and largely ineffective.Aims and Objectives:The aim of the project is to develop technology improving the way local authorities identify and manage potholes, and is designed to enable them to improve roads and reduce costs. The key objectives are:1. To develop affordable sensor technology to enable a vehicle mounted sensor travelling at speeds of up to 65Km/hr to identify and capture road damage between road kerb and centre line.2. To classify the road damage for reporting purposes.3. To provide an image of the damage.4. To provide an accurate location of the road damage.5. To create a "learning database" of typical damage parameters.6. To report the road damage on a store-and-forward or real-time basis.7. To capture and store data on a cloud based service.8. To map data so that it is easily understood.9. To provide web-based access to map based products.The project will initially focus on delivering a prototype for identifying, classifying, reporting and sharing information on potholes. Real-time applications will be developed following successful demonstration on the initial capability.Applications and benefits:The project will provide vehicle mounted sensors which can identify and classify potholes and other road damage, provide an accurate position for the damage to the road, report the occurrence by forwarding to cloud storage and enable the data to be accessed through a web browser showing the data on a map. We believe the most effective mechanism to deliver comprehensive mapping of local authority roads will be by mounting the sensors on refuge and recycling waste collection vehicles. This could be supplemented by standalone survey vehicles. Combining data from local authorities would provide comprehensive mapping of a region and potentially the UK.Benefits include:1. The creation of a detailed web-based mapping source of potholes in roads for highways agencies, local authorities and private subscribers. The data would include classification of damage, imaging and accurate positioning. This would enable highways authorities and local authorities to make informed decisions on the prioritisation of road repairs and provide private subscribers with a tool to avoid road damage to private vehicles.2. Real-time data capture and reporting of ground disturbances for military users in operational environments with a risk of improvised explosive device (IED) attack; enabling potential threats to be mitigated.3. Real-time data capture and reporting of natural and man-made hazards for unmanned precision farming vehicles; enabling avoidance action to be taken before impact.