[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83843-en":3,"doc-seo-83843-105":30,"detail-sidebar-cat-0-en-105":91},{"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},83843,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving","Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that stay reliable under degraded onboard sensing. Camera observations are often corrupted by occlusion, motion blur, illumination changes, and sensor noise, and indiscriminate temporal aggregation can destabilize trajectory planning and increase collision risk. RCT-AD models feature quality and temporal consistency: it scores per-frame reliability, selectively retains trustworthy features via a quality-gated FILO memory, and reconstructs degraded inputs from reliable context. A temporal planner with a joint detection-segmentation head supports smoother, safety-aware trajectories and strengthens BEV semantic understanding. Experiments on nuScenes show improved perception, motion prediction, and planning robustness.","A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving  \nArgho Dey, Yunfei Yin, Member, IEEE, Swachha Ray, Md Minhazul Islam, Zheng Yuan, Sijing Xiong, Hongyu  \nLiu, and Zhiqiu Huang  \nAbstract-Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded. In real driving conditions, camera observations are frequently corrupted by occlusion, motion blur, illumination change, and sensor noise, and when such degraded observations are aggregated indiscriminately over time, trajectory planning becomes unstable and collision risk rises for both the ego vehicle and surrounding road users. Recent Bird'sEye-View (BEV) approaches unify perception and planning through a shared spatial representation, but most fuse temporal information across frames without assessing the reliability of the underlying observations. We present a Reliable Context-Aware and Temporal Planning framework for Autonomous Driving (RCT-AD) that explicitly models feature quality and temporal consistency to support safer, more consistent planning. A Reliable Context Awareness module scores per-frame reliability and selectively retains trustworthy features through a quality-gated First-In-Last-Out (FILO) memory mechanism, reconstructing degraded observations from reliable historical context so that corrupted inputs do not destabilize the scene representation. A Temporal Trajectory Planner captures long-term dependencies and multi-agent interactions to produce smoother, safety-aware trajectories, while a joint detection-and-segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding. Experiments on the nuScenes autonomous driving benchmark show that RCT-AD improves perception accuracy, motion prediction, and planning robustness over recent end-to-end baselines, achieving 61.5 nuScenes Detection Score, 52.9 mean Average Precision, and 52.3 mean Intersection over Union, while maintaining competitive computational efficiency suitable for real-time deployment.  \nIndex Terms—Autonomous driving, Bird’s-Eye-View perception, temporal memory, semantic reasoning, trajectory planning, multitask learning.  \nI. INTRODUCTION  \nAarea of artificial intelligence [2], aiming to equip  \nUTONOMOUS driving has emerged as a transformative  \nvehicles with human-like perception, contextual understanding, and decision-making abilities for safe navigation in complex real-world environments. With the rapid  \nThis paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE. This work was supported in part by the National Natural Science Foundation of China under Grant 62262045, in part by the Fundamental Research Funds for the Central Universities under Grant 2023CDJYGRH-YB11, and in part by the Open Funding of SUGON Industrial Control and Security Center under Grant CUITSICSC-2025-03 . (Corresponding author: Yunfei Yin).  \nprogress of deep learning and multi-sensor perception technologies, modern autonomous vehicles have achieved remarkable improvements in environmental understanding and motion planning. Nevertheless, most existing autonomous driving systems still follow a modular pipeline that separates perception [13], prediction [24], [30] and planning [4], [10] into independent components. Although this modular design improves interpretability and allows each component to be optimized individually, it also disrupts the continuous flow of information across the pipeline. As a consequence, errors introduced during early perception stages can propagate to later modules, often resulting in degraded performance in dynamic and uncertain driving environments [17], [18] . Recent research has explored unified end-to-end autonomous driving frameworks that jointly model perception and planning within a single architecture. Approaches such as BridgeAD [7] attempt to bridge the","cbCaik6H1z6EcPdq","https://ap.wps.com/l/cbCaik6H1z6EcPdq","pdf",14609460,3,1,12,"English","en",105,"# Introduction\n## Motivation and challenges in perception-planning pipelines\n## Limits of existing end-to-end BEV aggregation and temporal modeling","[{\"question\":\"Why do degraded camera observations threaten autonomous driving safety?\",\"answer\":\"Occlusion, motion blur, illumination changes, and sensor noise can corrupt camera features. If such unreliable observations are aggregated across time, BEV representations become unstable, which can propagate to perception and planning and raise collision risk.\"},{\"question\":\"What reliability mechanism does RCT-AD use to handle corrupted temporal inputs?\",\"answer\":\"RCT-AD includes a Reliable Context Awareness module that scores per-frame feature reliability. It selectively keeps trustworthy features using a quality-gated First-In-Last-Out (FILO) memory and reconstructs degraded observations from reliable historical context.\"},{\"question\":\"How does RCT-AD improve trajectory quality and scene understanding?\",\"answer\":\"A Temporal Trajectory Planner captures long-term dependencies and multi-agent interactions to generate smoother, safety-aware trajectories. A joint detection-and-segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding.\"}]",1784190931,30,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-reliable-context-aware-and-temporal-planning-framework-for-autonomous-driving","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/a-reliable-context-aware-and-temporal-planning-framework-for-autonomous-driving/83843/",4,{"url":51,"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","2026-07-27","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do degraded camera observations threaten autonomous driving safety?","Question",{"text":75,"@type":76},"Occlusion, motion blur, illumination changes, and sensor noise can corrupt camera features. If such unreliable observations are aggregated across time, BEV representations become unstable, which can propagate to perception and planning and raise collision risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What reliability mechanism does RCT-AD use to handle corrupted temporal inputs?",{"text":80,"@type":76},"RCT-AD includes a Reliable Context Awareness module that scores per-frame feature reliability. It selectively keeps trustworthy features using a quality-gated First-In-Last-Out (FILO) memory and reconstructs degraded observations from reliable historical context.",{"name":82,"@type":73,"acceptedAnswer":83},"How does RCT-AD improve trajectory quality and scene understanding?",{"text":84,"@type":76},"A Temporal Trajectory Planner captures long-term dependencies and multi-agent interactions to generate smoother, safety-aware trajectories. A joint detection-and-segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]