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Pothole Identification and Management Autonomous System

Pothole Identification and Management Autonomous System
坑洞识别与管理自主系统
批准号:
EP/R005400/1
负责人:
Nabil Aouf
金额:
$19.23万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

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中文摘要
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英文摘要
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.
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会议论文
Autonomous Pothole Detection and Management System
自主坑洼检测和管理系统
DOI: --
发表时间: 2019
期刊:
影响因子: --
作者: [Alaa Alzoubi]
通讯作者: Alaa Alzoubi
Robotics & Remote Sensing for HMA & ERW Survey: Southeast Asia Feasibility Study with LMIC Collaborator Engagement
  • 批准号:
    ST/R002991/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $2.57万
  • 财政年份:
    2017
  • 负责人:
    Nabil Aouf
  • 依托单位:
国内基金
海外基金
Identification and quantification of primary phytoplankton functional types in the global oceans from hyperspectral ocean color remote sensing
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    160万元
  • 批准年份:
    2022
  • 负责人:
    李忠平
  • 依托单位: