[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82244-en":3,"doc-seo-82244-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82244,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Instrumentation and Field Tests to Evaluate a Rollover Risk Estimator for Mobile Machinery with Mobile Tools","Agricultural machines carrying mobile tools or implements exhibit internal dynamics that increase rollover susceptibility compared with fixed configurations. This work adapts real-time rollover risk estimation algorithms to such machinery by focusing on key metrics, especially the Load Transfer Ratio (LTR). Field testing on a self-propelled sprayer with a mobile ramp evaluates prediction performance and collects measurement data to calibrate a high-fidelity numerical vehicle model for simulated rollover accidents and accident-prevention assessment. Instrumentation, test procedures, and experimental design are detailed, including static parameter identification and dynamic real-time force and displacement measurements. Initial results show the algorithm captures LTR variations representing lateral and longitudinal rollover risk.","Instrumentation and field tests to evaluate a rollover risk estimator for mobile  \nmachinery with mobile tools  \nLama Al Bassit a,*, Bastien Laurent a, Philippe Heritier b , Romain Duval c , Valérie Sulis c  \nRoland Lenaina  \na INRAE, UR TSCF, Université Clermont Auvergne, 63178 Aubière, France b INRAE, UR TSCF, Université Clermont Auvergne, 03150 Montoldre, France  \nc CETIM, 60300 Senlis, France  \n* Corresponding author. Email: [lama.al-bassit@inrae.fr](lama.al-bassit@inrae.fr)  \nAbstract  \nAgricultural machines that carry mobile tools or implements have an internal dynamic that makes them more prone to rollover risk than machines with fixed configurations. Previous research has led to the development of algorithms capable of predicting rollover risk in real time by estimating relevant metrics , primarily the LTR (Load Transfer Ratio) , with a focus on vehicles with static configurations. This paper presents the adaptation of these algorithms for machines with mobile tools and describes the field tests, conducted on a self-propelled sprayer, to evaluate their effectiveness in predicting rollover. The tests also aimed to acquire the necessary data to build an accurate numerical model of the vehicle and produce high-quality simulated tests. This paper describes the chosen instrumentation, the test procedure, and the experimental design of these tests. These field tests include static tests and dynamic tests. Static tests determine vehicle parameters, while dynamic tests measure variables related to the machine displacement and wheel/ground forces in real time. The initial results demonstrate the algorithms’ ability to capture variations in LTR, which express lateral and longitudinal rollover risk. The data collected through these tests will enable the vehicle displacements to be replayed by simulation, producing simulated rollover accidents and evaluating the effectiveness of the developed algorithms in accident prevention.  \nKeywords: Stability of mobile machinery, Mobile machinery with mobile implements, Risk of overturning, Load Transfer Ratio, Measurement of wheel/ground forces.  \n1. Introduction  \nRollover accidents involving mobile machinery are one of the main causes of occupational fatalities in the agricultural sector (Rondelli et al. , 2018; Wang et al. , 2024) . Beyond formation to improve driver skills and safety procedure, technical solutions proposed to reduce rollover risk can be classified into two broad categories offering different levels of safety. The first comprises solutions designed to mitigate the consequences of a rollover accident, while the second comprises solutions designed to prevent them, offering a higher level of safety. The first category includes passive and active rollover protection structures (ROPS), which create a safety zone around machinery occupants and prevent crushing injuries in the event of a rollover (Franceschetti et al. , 2019; Ojados-Gonzalez et al. , 2016) . It also includes systems that can detect rollovers and alert a remote rescue centre (Liu et al. , 2013) . The second category includes alarm systems that alert the driver to imminent rollover risk, enabling them to take appropriate action to prevent it (Hu et al. , 2019) . This category also includes systems that autonomously modify the vehicle's controlled variables (e.g. speed, steering and active suspension) to prevent rollover accidents (Richier, 2014; Zhu and Kan, 2022; Gao et al. , 2022) . For some technical solutions in both categories, especially the more effective ones in terms of safety, it is essential to estimate rollover imminence and predict hazardous situations.  \nTo reduce the risk of agricultural machinery overturning, the INRAE TSCF research unit has been developing for several years real-time algorithms that estimate the rollover risk of outdoor machinery (Bouton et al. , 2008; Richier et al. , 2014) . These algorithms have been tested in various conditions and on different types of vehicle without implem","cbCaikIXvZVfo0fD","https://ap.wps.com/l/cbCaikIXvZVfo0fD","pdf",1519348,1,11,"English","en",105,"# Introduction\n# Materials and Methods\n## Online stability estimation and rollover prevention adaptation\n## Instrumentation and test procedure","[{\"question\":\"How do the field tests support both algorithm validation and numerical model calibration?\",\"answer\":\"The tests collect static and dynamic measurements: static tests identify vehicle parameters, while dynamic tests capture displacement-related variables and wheel/ground forces in real time. The collected data are used to replay vehicle displacements in simulation, generate simulated rollover scenarios, and evaluate prevention effectiveness.\"}]",1784179101,28,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"instrumentation-and-field-tests-to-evaluate-a-rollover-risk-estimator-for-mobile-machinery-with-mobile-tools","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/instrumentation-and-field-tests-to-evaluate-a-rollover-risk-estimator-for-mobile-machinery-with-mobile-tools/82244/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"How do the field tests support both algorithm validation and numerical model calibration?","Question",{"text":74,"@type":75},"The tests collect static and dynamic measurements: static tests identify vehicle parameters, while dynamic tests capture displacement-related variables and wheel/ground forces in real time. The collected data are used to replay vehicle displacements in simulation, generate simulated rollover scenarios, and evaluate prevention effectiveness.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general"]