[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-232787-105":59,"doc-detail-232787-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","maneuver-identification-challenge-paper-v1-overview","Maneuver Identification Challenge - Paper v1 - Overview","","AI algorithms that identify maneuvers from trajectory data can improve flight safety and pilot training. The Maneuver Identification Challenge, hosted by MIT, supplies thousands of simulator trajectories, maneuver descriptions, and examples from experienced pilots. Each trajectory includes normalized positions, velocities, and aircraft orientations, enabling machine learning over consistent coordinate frames. Building the dataset required substantial data architecture, including supercomputer-based deduplication and data conditioning. Three challenges are proposed: separating plausible from implausible trajectories, labeling trajectories by intended maneuvers, and evaluating maneuver quality.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/maneuver-identification-challenge-paper-v1-overview/232787/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/maneuver-identification-challenge-paper-v1-overview/232787.png","ImageObject",300,407,{"name":92,"@type":93},"Logic","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-10",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the Maneuver Identification Challenge address?","Question",{"text":113,"@type":114},"It targets AI methods that identify flight maneuvers from trajectory data, aiming to support safer flight operations and improved pilot training.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What data does the challenge provide for participants?",{"text":118,"@type":114},"Participants receive thousands of trajectories from flight simulators, along with maneuver descriptions and examples performed by experienced pilots. Trajectories include normalized positions, velocities, and aircraft orientations.",{"name":120,"@type":111,"acceptedAnswer":121},"What are the three proposed challenges in this competition?",{"text":122,"@type":114},"First, separate physically plausible (good) trajectories from unfeasible (bad) trajectories using human-labeled examples. Then, label trajectories with their intended maneuvers. Finally, assess the quality of those labeled maneuvers.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},232787,1789073648,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},1099513958762,"https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253","Maneuver Identification Challenge  \nKaira Samuel, Vijay Gadepally, David Jacobs, Michael Jones, Kyle McAlpin, Kyle Palko, Ben Paulk, Sid Samsi, Ho Chit Siu, Charles Yee, Jeremy Kepner  \nMIT  \narXiv :2108 . 11503v1 [ cs .AI] 25 Aug 2021  \nAbstract—AI algorithms that identify maneuvers from trajectory data could play an important role in improving flight safety and pilot training. AI challenges allow diverse teams to work together to solve hard problems and are an effective tool for developing AI solutions. AI challenges are also a key driver of AI computational requirements. The Maneuver Identification Challenge hosted [at maneuver-id.mit.edu](at maneuver-id.mit.edu) provides thousands of trajectories collected from pilots practicing in flight simulators, descriptions of maneuvers, and examples of these maneuvers performed by experienced pilots. Each trajectory consists of positions, velocities, and aircraft orientations normalized to a common coordinate system. Construction of the data set required significant data architecture to transform flight simulator logs into AI ready data, which included using a supercomputer fordeduplication and data conditioning. There are three proposed challenges. The first challenge is separating physically plausible (good) trajectories from unfeasible (bad) trajectories. Human labeled good and bad trajectories are provided to aid in this task. Subsequent challenges are to label trajectories with their intended maneuvers and to assess the quality of those maneuvers.  \nIndex Terms—artificial intelligence, trajectory optimization,flight maneuvers, pilot training  \nI. INTRODUCTION  \nAI challenges developed by AI experts are a primary tool for advancing AI and engaging the AI ecosystem on important problems. AI challenges are also a key driver of AI computational requirements. Challenges such as YOHO [1], MNIST [2], HPC Challenge [3], Graph Challenge [4]–[6], ImageNet [7] and VAST [8], [9] have played important roles in driving progress in fields as diverse as machine learning, high performance computing, and visual analytics. YOHO is the Linguistic Data Consortium database for voice verification systems and has been a critical enabler of speech research. The MNIST database of handwritten letters has been a bedrock of the computer vision research community for two decades. HPC Challenge has been used by the supercomputing community to benchmark and test the largest supercomputers in the world as well as stimulate research on the new parallel programming environments. Graph Challenge has led to the development of award winning software and hardware [10]–[12] . Ima  \nThis material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702- 15-D-0001, National Science Foundation CCF-1533644, and United States Air Force Research Laboratory Cooperative Agreement Number FA8750-19- 2-1000. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Assistant Secretary of Defense for Research and Engineering, the National Science Foundation, or the United States Air Force. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.  \n1 Team 1 Problem Win Probability  \n  ~20%  \n10 Problems  \nWin Probability  \n~200%(~2 solutions to 10 problems)  \n10 Problems  \n10 Challenges 100s of Teams  \nWin Probability  \n>>1000%(many solutions to each problem)  \nFig. 1. Challenging AI problems are best solved by enabling diverse teams to work on them. The MIT Air Force AI Accelerator teams are creating many challenges to engage the entire AI ecosystem and maximize the number of solutions.  \ngeNet populated an image dataset according to the WordNet hierarchy consisting of over 100,000 meaningful concepts (called synonym sets or synsets) [7], with an average of 1000 images per synset, and ha","cbCairaA9QbYAgbK","https://ap.wps.com/l/cbCairaA9QbYAgbK","pdf",956626,"English","# Abstract\n# Introduction\n## Background on AI challenges\n# Maneuver Identification Challenge Overview\n## Data set and challenges","[{\"question\":\"What problem does the Maneuver Identification Challenge address?\",\"answer\":\"It targets AI methods that identify flight maneuvers from trajectory data, aiming to support safer flight operations and improved pilot training.\"},{\"question\":\"What data does the challenge provide for participants?\",\"answer\":\"Participants receive thousands of trajectories from flight simulators, along with maneuver descriptions and examples performed by experienced pilots. Trajectories include normalized positions, velocities, and aircraft orientations.\"},{\"question\":\"What are the three proposed challenges in this competition?\",\"answer\":\"First, separate physically plausible (good) trajectories from unfeasible (bad) trajectories using human-labeled examples. Then, label trajectories with their intended maneuvers. Finally, assess the quality of those labeled maneuvers.\"}]","Maneuver Identification Challenge - Paper v1 - Overview | PDF",18]