{"id":4465,"date":"2026-03-04T15:10:29","date_gmt":"2026-03-04T14:10:29","guid":{"rendered":"https:\/\/pharos390.com\/?p=4465"},"modified":"2026-03-04T16:21:41","modified_gmt":"2026-03-04T15:21:41","slug":"automation-of-vehicles-in-port-environments","status":"publish","type":"post","link":"https:\/\/pharos390.com\/en\/automation-of-vehicles-in-port-environments\/","title":{"rendered":"Automation of vehicles in port environments"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-background has-link-color wp-elements-1ab44a34ac15a5959f5fd1227555966f wp-block-paragraph\" style=\"background-color:#abb7c23d;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">The <strong>automation of vehicles<\/strong> in port environments is one of the key elements of modern logistics, enabling smarter and more connected ports in a context of growing global demand and pressure for sustainability. This content is an adapted and summarised version of the paper <em>\u2018Collaborative Localisation and Perception in Port Scenarios: CleanPorts 5.0<\/em>\u2019 presented at the <a href=\"https:\/\/2025.itseuropeancongress.com\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/2025.itseuropeancongress.com\/\" rel=\"noreferrer noopener\"><strong>16th European Congress for Leaders in Smart Mobility and Intelligent Transport Systems<\/strong><\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-9314b9e7637f4cf69d37de218300971c wp-block-paragraph\">Ports are key hubs for global trade, handling approximately <strong>75% of internationally traded goods<\/strong>. These environments are home to numerous vehicles\u2014container trucks, autonomous guided vehicles (AGVs) and other specialised equipment\u2014which must operate in a coordinated manner in often confined spaces with high traffic density.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-6e592eb31fafa53a0126f25d0baed4d5 wp-block-paragraph\">However, navigating and locating these vehicles is not always easy. Traditional <strong>GNSS<\/strong>-based systems (<strong>such as GPS<\/strong>) can lose accuracy in port terminals due to the presence of large cranes, metal structures and stacks of containers, which cause signal interference.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-1ad8a9abd4ee6ca70dc70df2845fddc7 wp-block-paragraph\">In this context, having more <strong>advanced location and perception systems<\/strong> is essential to ensure safe and efficient operations. One of the emerging solutions is <strong>infrastructure-based perception<\/strong>, where sensors installed in the port itself monitor the environment and provide a global view of what is happening in the terminal.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-d3104445f48c131bbdbbfa989b307dee wp-block-paragraph\">This approach complements the systems installed in vehicles, improving obstacle detection, facilitating traffic coordination and reducing the risk of collisions. In short, it enables progress towards safer, <strong>more efficient port operations that are ready for automation<\/strong>.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-a6e0a2609c94a3dbf970412e590a6956 wp-block-paragraph\">In port environments, there are <strong>three main challenges<\/strong> that this research seeks to address:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"has-black-color has-text-color has-link-color wp-elements-760dbbedbaff0e5aaed0f3667a897fbd\"><strong>Interference with positioning signals<\/strong>: Large metal structures, cranes and stacks of containers can interfere with satellite positioning signals (<strong>GNSS<\/strong>), reducing their accuracy.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"has-black-color has-text-color has-link-color wp-elements-119adb7dd5ece0c33132457cecab8168\"><strong>Traffic complexity<\/strong>: High-density traffic requires precise obstacle detection and avoidance capabilities.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"has-black-color has-text-color has-link-color wp-elements-24d26c3cb5f76bfdecfdc97332731f6b\"><strong>Cooperation requirements<\/strong>: vehicles and infrastructure must share information to improve situational awareness and safety.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-b4c1c8a267d17a2d56c849b07c1f9a59 wp-block-paragraph\">The dynamic nature of port environments, where the position of containers changes frequently and a large number of vehicles circulate, limits the use of simultaneous <strong>localisation and mapping (SLAM) techniques<\/strong>, which tend to work better in more stable environments. In this context, <strong>LiDAR odometry (LiO)<\/strong> emerges as an effective alternative, as it focuses on estimating the position of the vehicle without the need to generate a complete global map. This makes it particularly suitable for dynamic environments with few fixed references, such as ports, where the main objective is to ensure accurate localisation.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-c20bbb90907e3b6b4e9e4954ba17b8b8 wp-block-paragraph\"><strong>LiDAR odometry<\/strong> allows the position and orientation of a vehicle to be estimated by analysing consecutive LiDAR scans. This system can operate independently or be combined with other sensors, such as <strong>inertial measurement units (IMUs)<\/strong>, which record accelerations and angular movements at high frequency. This combination helps to reduce distortions in <strong>LiDAR <\/strong>scans caused by vehicle movement.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-3d46d401ce2b12f581d2d9ef34aaf716 wp-block-paragraph\">This study uses <strong>DLIO (Direct LiDAR-Inertial Odometry)<\/strong>, an advanced solution that combines <strong>LiDAR<\/strong> and inertial data to improve vehicle trajectory estimation with lower computational load. The system is complemented by <strong>RTK-GNSS<\/strong>, which provides accurate initial positioning, while DLIO allows for continuous and more robust location tracking even in areas where the GNSS signal is degraded.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-10b32de9ddf533d61265432937712a4a wp-block-paragraph\">To evaluate this approach, infrastructure-based perception is implemented in the <strong>CARLA<\/strong> simulator, using a <strong>LiDAR sensor<\/strong> located at a fixed point on the infrastructure within a simulated port area. The environment reproduces a port scenario with multiple vehicles, including lorries and cars, travelling along different routes.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-671f911b0a07d4e3205251c5c628dc4b wp-block-paragraph\">Based on the data captured by <strong>LiDAR<\/strong>, the <strong>PointPillars<\/strong> model is applied, a deep learning-based 3D object detection system designed to process <strong>LiDAR<\/strong> point clouds quickly and efficiently. Subsequently, a real-time tracking algorithm (<strong>SORT<\/strong>) is used to maintain the identification of each vehicle over time, allowing continuous tracking of moving vehicles. The <strong>LiDAR<\/strong> installed in the infrastructure thus provides a <strong>comprehensive view of the environment<\/strong>, capable of detecting and monitoring multiple vehicles simultaneously.<\/p>\n\n\n\n<p class=\"has-black-color has-text-color has-link-color wp-elements-57cfd924a6d5f4db506c528637f4ce3b wp-block-paragraph\">This approach demonstrates the potential of combining <strong>on-board perception and infrastructure-based perception <\/strong>to facilitate autonomous navigation in port environments, improving safety, traffic coordination and operational efficiency. Furthermore, such solutions could also be applied to other industrial environments characterised by high vehicle density.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h3 class=\"wp-block-heading has-luminous-vivid-amber-color has-text-color has-link-color wp-elements-dc39df78cded1e73118da200b840e750\">References<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"has-black-color has-text-color has-link-color wp-elements-12959fc4bae3472474f37ceaa2c810e3\">Justo, A.; Murgoitio, J.; Soler, A.; Palomo, P.; Mart\u00edn, \u00c1. (2025). <em>Collaborative Localization and Perception in Port Scenarios: CleanPorts 5.0<\/em>. 16th ITS European Congress, Seville, Spain, 19\u201321 May 2025.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"has-small-font-size wp-block-paragraph\"><em>*Disclaimer: This English version has been generated with the support of AI-based translation tools. In case of discrepancies, the Spanish original prevails.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The automation of vehicles in port environments is one of the key elements of modern logistics, enabling smarter and\u2026<\/p>\n","protected":false},"author":27,"featured_media":4472,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[75,127,1],"tags":[76,121,86],"tipo_publicacion":[],"temas":[],"ano_publicacion":[63],"class_list":["post-4465","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-port-infrastructure","category-papers","category-sin-categoria","tag-automation","tag-featured_papers","tag-ports","ano_publicacion-63"],"primary_category_id":75,"coauthors_data":[],"_links":{"self":[{"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/posts\/4465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/users\/27"}],"replies":[{"embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/comments?post=4465"}],"version-history":[{"count":7,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/posts\/4465\/revisions"}],"predecessor-version":[{"id":4490,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/posts\/4465\/revisions\/4490"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/media\/4472"}],"wp:attachment":[{"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/media?parent=4465"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/categories?post=4465"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/tags?post=4465"},{"taxonomy":"tipo_publicacion","embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/tipo_publicacion?post=4465"},{"taxonomy":"temas","embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/temas?post=4465"},{"taxonomy":"ano_publicacion","embeddable":true,"href":"https:\/\/pharos390.com\/en\/wp-json\/wp\/v2\/ano_publicacion?post=4465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}