[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85968-en":3,"doc-seo-85968-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"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},85968,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","BucketKD: A Safety-Aware Bucket-Based Knowledge Distillation Framework for End-to-End Motion Planning","End-to-end motion planning directly maps raw sensor inputs to driving control through deep neural networks, but the approach is often blocked by large model size that limits deployment on resource-constrained platforms. BucketKD introduces bucket-based knowledge distillation that compresses the planner while remaining safety-aware. It discretizes critical environmental variables into adaptive, nonuniform buckets for richer scene semantics. A safety-aware waypoint attention mechanism uses time-to-collision risk and relative motion to preserve safety-critical behaviors. Experiments on CARLA with Bench2Drive show improved planning accuracy and safety with strong compression.","BucketKD: A Safety-Aware Bucket-Based Knowledge Distillation Framework for End-to-End Motion Planning  \nMd Nahidul Islam2 , Mohd Hasan Ali1 , Dipankar Dasgupta2 , and Myounggyu Won2  \narXiv :2607 . 10565v1 [ cs .RO] 12 Jul 2026  \nAbstract—End-to-end motion planning has emerged as a promising paradigm in autonomous driving, directly mapping raw sensor data to control commands via deep neural networks. Despite its advantages, its large model size hinders deployment in resource-constrained platforms. In this paper, we present BucketKD, a bucket-based knowledge distillation framework that yields compact and safety-aware end-to-end planners. Compared to the state-of-the-art approach, which relies onsimplified planning state representations, BucketKD discretizes critical environmental variables into adaptive buckets that capture richer scene semantics while preserving efficiency. In addition, we design a safety-aware waypoint attention mechanism that evaluates each waypoint’s risk level by accounting for both obstacle proximity and relative motion through a timeto-collision (TTC) formulation widely used in transportation research. This enables the student model to better retain safetycritical behaviors during distillation. Extensive experiments in CARLA using the Bench2Drive dataset show that BucketKDsignificantly outperforms the state-of-the-art in both planning accuracy and safety while maintaining strong compression ratios.  \nI. INTRODUCTION  \nEnd-to-end motion planning has emerged as a promising paradigm in autonomous driving. In this approach, deep neural networks learn an end-to-end mapping from raw sensor data to the vehicle’s trajectory or control actions, removing the need for explicit perception, planning, and control submodules [1], [2], [3], [4]. End-to-end motion planning offers reduced latency, greater efficiency, and stronger generalization than traditional modular pipelines by integrating perception, prediction, and planning into a single data-driven model [5], [6] . However, a key challenge of this approach is its large model size, which typically demands highperformance onboard hardware, making deployment difficult in resource-constrained environments such as autonomous delivery robots or vehicles with limited computational capacity [2] .  \nTo mitigate the model size limitation, a state-of-theart compression framework PlanKD leverages knowledge distillation to achieve compact yet high-performing endto-end motion planners [2] . Inspired by the bottleneck principle [7], PlanKD learns compact representations that  \n1Mohd Hasan Ali is with the Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, United States [mhali@memphis.edu](mhali@memphis.edu)  \n2Md Nahidul Islam, Dipankar Dasgupta, and Myounggyu Won are with the Department of Computer Science, University of Memphis, Memphis, TN, United States {mislam19, ddasgupt, [mwon](mwon}@memphis.edu)[}](mwon}@memphis.edu)[@memphis.edu](mwon}@memphis.edu)  \ncapture planning-critical features with minimal dimensionality while maintaining sufficient information for robust decision-making. While PlanKD achieves substantial model compression without notable performance degradation, the oversimplification of its planning state representation constrains its capability to handle complex and dynamic driving scenarios. Furthermore, its safety model’s dependence on distance-based metrics alone limits its ability to assess the full contextual risk of the environment, thereby increasing the likelihood of suboptimal or unsafe maneuvers.  \nIn this paper, we present BucketKD, a Bucket-based Knowledge Distillation framework for end-to-end motion planning in autonomous driving. Given the limited generalization of binary state representations in existing knowledge distillation frameworks, especially under complex and dynamically evolving traffic conditions, we introduce a bucketbased method that discretizes each critical state variable into a set of nonuniform buckets ","cbCaiqj7AizQCRAZ","https://ap.wps.com/l/cbCaiqj7AizQCRAZ","pdf",445533,3,1,"English","en",105,"# Introduction\n## End-to-end motion planning overview\n## Model compression via knowledge distillation\n## Limitations of prior approaches\n# Proposed method: BucketKD\n## Bucket-based planning state formulation\n## Safety-aware waypoint attention with TTC\n# Experimental evaluation","[{\"question\":\"What problem does BucketKD address in end-to-end motion planning?\",\"answer\":\"BucketKD targets the large-model requirement of end-to-end planners that makes deployment difficult on limited hardware, while also improving safety during distillation.\"},{\"question\":\"How does BucketKD improve planning-state representation compared with prior distillation methods?\",\"answer\":\"It discretizes critical environmental variables into adaptive nonuniform buckets, capturing finer-grained behavioral variations without making the state space unmanageable.\"},{\"question\":\"What makes BucketKD safety-aware during knowledge distillation?\",\"answer\":\"It uses a safety-aware waypoint attention mechanism that assigns risk based on obstacle proximity and relative motion through a time-to-collision (TTC) formulation, helping the student retain safety-critical behaviors.\"}]",1784207453,20,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":27},"bucketkd-a-safety-aware-bucket-based-knowledge-distillation-framework-for-end-to-end-motion-planning","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":21},"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":20},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/bucketkd-a-safety-aware-bucket-based-knowledge-distillation-framework-for-end-to-end-motion-planning/85968/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does BucketKD address in end-to-end motion planning?","Question",{"text":74,"@type":75},"BucketKD targets the large-model requirement of end-to-end planners that makes deployment difficult on limited hardware, while also improving safety during distillation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does BucketKD improve planning-state representation compared with prior distillation methods?",{"text":79,"@type":75},"It discretizes critical environmental variables into adaptive nonuniform buckets, capturing finer-grained behavioral variations without making the state space unmanageable.",{"name":81,"@type":72,"acceptedAnswer":82},"What makes BucketKD safety-aware during knowledge distillation?",{"text":83,"@type":75},"It uses a safety-aware waypoint attention mechanism that assigns risk based on obstacle proximity and relative motion through a time-to-collision (TTC) formulation, helping the student retain safety-critical 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