{"id":273,"date":"2025-12-11T21:18:48","date_gmt":"2025-12-11T21:18:48","guid":{"rendered":"https:\/\/people.utm.my\/hoofar\/?page_id=273"},"modified":"2025-12-19T10:05:14","modified_gmt":"2025-12-19T10:05:14","slug":"book","status":"publish","type":"page","link":"https:\/\/people.utm.my\/hoofar\/book\/","title":{"rendered":"“IN \u2013 FLEET MONITORING” BOOK"},"content":{"rendered":"\n
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A tentative coversheet of the book, which includes seven chapters that discuss traffic monitoring and roadway monitoring using CAV data.<\/figcaption><\/figure>\n<\/div><\/div>\n\n\n\n
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The tentative book title, \u201cIn-Fleet Monitoring: Digital-Twin-Enabled Roadway Monitoring and Traffic Management via Connected Autonomous Vehicles\u2019 Data,\u201d<\/strong> has been selected to clarify and explain the conceptual framework and capabilities of the proposed In-Fleet monitoring<\/a> approach. The volume will be developed under the editorship of the project\u2019s principal investigator, <\/a><\/strong>Hoofar<\/strong> Shokravi<\/strong><\/a>, and Kevin Vincent<\/a><\/strong>, Director of the Centre for Connected and Autonomous Automotive Research and the National Transport Design Centre at Coventry University. While existing scholarly work predominantly focuses on vehicle-centric safety applications of connected autonomous vehicles (CAVs)\u2014such as road perception, localization, and inter-vehicle communication\u2014no prior traffic-centric research has systematically leveraged real-time CAV data streams to develop a unified, scalable framework for proactive roadway condition monitoring and adaptive traffic management. <\/p>\n<\/div>\n<\/div>\n\n\n\n

To our knowledge, this represents the first effort to integrate CAV-derived data holistically across infrastructure networks, transcending isolated safety applications to address system-wide efficiency and resilience. This work addresses critical gaps in modern intrusion detection systems by introducing a cyber-physical platform that fulfills all five pillars of Industry 4.0 and Smart City frameworks:<\/p>\n\n\n\n

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  1. Real-time responsiveness<\/strong><\/li>\n\n\n\n
  2. Network-wide coverage<\/strong><\/li>\n\n\n\n
  3. Continuous operation<\/strong><\/li>\n\n\n\n
  4. Crowdsourced data integration<\/strong><\/li>\n\n\n\n
  5. Full automation<\/strong><\/li>\n<\/ol>\n\n\n\n

    While existing methods (e.g., stationary CCTV, IoT sensors) excel at real-time and automated detection, they lack scalability, crowdsourcing, and holistic network integration\u2014limitations that undermine their utility in adaptive traffic management. Our platform uniquely leverages CAVs as dual-purpose sensor-actuator networks<\/em>, combining their multimodal perception systems (LiDAR, GNSS, IMUs, cameras) and V2X connectivity to achieve sub-meter localization accuracy<\/strong> and system-wide interoperability<\/strong>. Building on the In-Fleet<\/em> framework for bridge health monitoring, we expand CAVs\u2019 role to enable:<\/p>\n\n\n\n

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    1. Proactive roadway diagnostics<\/strong>: Early detection of pavement distress, debris, and congestion via direct vehicle-pavement interaction and crowdsourced LiDAR\/telemetry data.<\/li>\n\n\n\n
    2. Adaptive traffic control<\/strong>: Real-time rerouting and speed adjustments using network-wide CAV-derived traffic flow analytics.<\/li>\n\n\n\n
    3. Cost-efficient scalability<\/strong>: Elimination of dedicated infrastructure through the reuse of CAV sensor data across jurisdictions.<\/li>\n<\/ol>\n\n\n\n

      This integration of mobile sensing<\/strong>, edge computing<\/strong>, and participatory data fusion<\/strong> establishes the first end-to-end solution for roadway monitoring and traffic management that concurrently satisfies all five Industry 4.0 requirements.<\/p>\n","protected":false},"excerpt":{"rendered":"

      The tentative book title, \u201cIn-Fleet Monitoring: Digital-Twin-Enabled Roadway Monitoring and Traffic Management via Connected Autonomous Vehicles\u2019 Data,\u201d has been selected to clarify and explain the conceptual framework and capabilities of the proposed In-Fleet monitoring approach. The volume will be developed … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":25744,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-273","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/pages\/273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/users\/25744"}],"replies":[{"embeddable":true,"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/comments?post=273"}],"version-history":[{"count":26,"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/pages\/273\/revisions"}],"predecessor-version":[{"id":385,"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/pages\/273\/revisions\/385"}],"wp:attachment":[{"href":"https:\/\/people.utm.my\/hoofar\/wp-json\/wp\/v2\/media?parent=273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}