[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82982-en":3,"doc-seo-82982-105":30,"detail-sidebar-cat-0-en-105":83},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82982,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","IMR Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction","Multi-agent motion prediction is vital for automated vehicles to infer surrounding agents’ intentions, yet existing approaches suffer from limited mode diversity or reduced prediction accuracy, undermining safety assessment and behavior alignment. A mode-world weighted regression loss is introduced to mitigate mode collapse while improving world ranking and top-1 confidence. An iterative decoder further enhances accuracy by recurrently and segmentally generating trajectories. Experiments show top performance on the Argoverse 2 multi-agent forecasting benchmark.","IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory  \nPrediction  \nHonglin Wang EACON Fujian, China  \n[wanghonglin921@gmail.com](wanghonglin921@gmail.com)  \nShiyao Pan EACON Fujian, China  \n[dcspsy@gmail.com](dcspsy@gmail.com)  \nYun-Fu Liu EACON Fujian, China  \n[yunfuliu@gmail.com](yunfuliu@gmail.com)  \narXiv :2607 .05705v 1 [ cs .RO] 6 Jul 2026  \nAbstract  \nMulti-agent motion prediction is essential for automated vehicles to understand the intentions of surrounding vehicles. However, previous prediction-based and anchor-based methods have limitations in mode diversity and prediction accuracy, respectively. These limitations may cause inadequate safety assessments and behavioral deviations in automated vehicles. To address this issue, a mode-world weighted regression loss is proposed to bridge the gap between these features. Specifically, this approach mitigates mode collapse while simultaneously improving world ranking and top-1 confidence. Furthermore, the proposed iterative decoder improves prediction accuracy by recurrently and segmentally generating trajectories. Experimental results show the proposed method ranks first in theArgoverse 2 multi-agent motion forecasting benchmark against other methods.  \n1. Introduction  \nMotion prediction is crucial for autonomous driving technology, establishing the foundation for achieving high reliability and safety in autonomous vehicles [2], [3] . Through accurate prediction of future trajectories of road entities (e.g., vehicles, bicycles, pedestrians), it facilitates real-time environmental perception and dynamic analysis. This capability enhances path planning and obstacle avoidance precision, mitigates accident risks, and improves overall driving safety, contributing to smoother navigation.  \nPrevious prediction-based methods (e.g., QCNet [10] and Forecast-MAE [1]) in multi-agent motion prediction are prone to mode collapse in complex scenarios. Anchorbased approaches (e.g., MTR [5] and TNT [9]) mitigate this issue at the expense of prediction accuracy. To resolve this trade-off, we propose a mode-world weighted regression loss within the prediction-based framework. Experiments demonstrate enhanced mode diversity without compromis-  \ning accuracy, thereby bridging the performance gap across paradigms. Crucially, it mitigates mode collapse while concurrently improving the accuracy of world ranking and top- 1 confidence.  \nFurthermore, we note that the previous state-of-theart method QCNeXt [11] on the Argoverse 2 [7] multiagent motion forecasting benchmark, employs a proposalrefinement decoding architecture. This method further refines proposed trajectories to generate more accurate predictions. However, when initial trajectories substantially deviate from ground truth, refinement layers struggle to effectively capture correct offsets due to excessive errors. Consequently, we propose an iterative decoder, and each iteration directly outputs trajectory coordinates rather than offsets. Simultaneously, encoded features, decoded outputs, and predicted trajectory from each iteration is propagated to next iteration, maximizing intermediate information utilization. This recurrent and segmented method facilitates iterative trajectory optimization.  \n2. Approach  \nThe proposed method is introduced in this section. First, the scene representation approach and encoder design are described. Second, the architecture and components of the iterative decoder are elaborated. Finally, the mode-world weighted regression loss is presented.  \n2.1. Scene representation and encoder  \nWe treat the historical trajectory of each agent at everytimestep as an independent element. Using the element asthe origin, we construct a polar coordinate system and select agents within a preset radius as interaction objects. For each interaction pair, we compute 3D spatial relationships (relative distance, azimuth, orientation) to construct a dynamic attention weighted matrix.  \nThe graph attention n","cbCairCKwnvIwDKR","https://ap.wps.com/l/cbCairCKwnvIwDKR","pdf",1001285,4,1,5,"English","en",105,"# Abstract\n# Introduction\n# Approach\n## Scene representation and encoder\n## Iterative decoder","[{\"question\":\"What benchmark results does the method achieve?\",\"answer\":\"The method ranks first on the Argoverse 2 multi-agent motion forecasting benchmark compared with other approaches.\"}]",1784184442,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"imr-iterative-mode-world-weighted-regression-for-multi-agent-trajectory-prediction","",{"@graph":36,"@context":77},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/imr-iterative-mode-world-weighted-regression-for-multi-agent-trajectory-prediction/82982/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What benchmark results does the method achieve?","Question",{"text":75,"@type":76},"The method ranks first on the Argoverse 2 multi-agent motion forecasting benchmark compared with other approaches.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":84},[85,89,93,97,101,106,111,114,119,122,126],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":22,"slug":129},19,"General","general"]