[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82245-en":3,"doc-seo-82245-105":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},82245,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Empirical Pedestrian Safety Assessment in a Mobile Robot Using a Predictive Social Force Model","Mobile robots are expected to share sidewalks with pedestrians and must balance objective safety with pedestrians’ subjective comfort. Computationally efficient Social Force Models (SFM) enable interpretable, real-time navigation in dynamic crowds. The study revises PTTC-enhanced SFM by introducing Predictive SFM (PSFM) and Predictive TSFM (PTSFM) through integration of predicted social-force vectors over a finite horizon. Implemented variants are evaluated on an on-nonholonomic robot using volunteer experiments for both objective metrics and Likert-based subjective ratings.","1  \nEmpirical Pedestrian Safety Assessment in a Mobile Robot Using a  \nPredictive Social Force Model  \nAlireza Jafari, Yun-Hao Tsai, and Yen-Chen Liu Senior Member, IEEE  \narXiv :2607 .09192v1 [ cs .RO] 10 Jul 2026  \nAbstract—Mobile robots are going to share the sidewalks with pedestrians. They must ensure their objective safety and respect the walkers’ subjective safety/comfort. Computationally efficient Social Force Models (SFM) present interpretable solutions for realtime robot navigation in dynamic crowds. Recent explorations of Projected Time-to-collision (PTTC) integration into SFM variants, for example, PTTC-based SFM (TSFM), improve safety metrics. But the effect of predictive variants is unclear. We introduce Predictive SFM (PSFM) and Predictive TSFM (PTSFM) by integrating predicted social force vectors over a finite time horizon. The paper implements SFM, TSFM, PSFM, and PTSFM on anonholonomic mobile robot and performs experimental trials with volunteers attending a facing scenario. We systematically study objective and subjective safety across the variants. Minimum PTTC, average speed, minimum distance, lateral distance, and the maximum trajectory curvature benchmark the objective safety. Likert scale post-interaction surveys assess subjective safety by marking comfort, smoothness, distance appropriateness, and speed suitability. We confirm that PTTC integration improves safety metrics. The prediction contribution is limited and occasionally visible in some of the sub-metrics. Some participants perceive smoother movements and safer speed behavior with predictive methods, but Mann-Whitney tests reveal no significant differences in subjective ratings. Therefore, PTTC-based navigation enhances safety, whereas the formulated prediction offers limited additional benefits in single-pedestrian scenarios.  \nIndex Terms—Pedestrian–robot interaction, social force model, projected time-to-collision, predictive navigation, objective safety, subjective comfort.  \nI. INTRODUCTION  \nPEDESTRIANS are going to share  \nmobile robots. Examples of the  \ntheir public spaces with mobile robots entering  \npublic spaces are delivery robots on sidewalks, service robots in hospitals [1], robotic bodyguards in shopping malls [2], and companion robots in leisure spaces [3] . The integration of robots into public spaces challenges people’s safety. Two aspects of human safety are objective safety and subjective safety. Objective safety quantifies the physical contact risks  \nThis work was supported in part by the National Science and Technology Council (NSTC), Taiwan, under Grants NSTC 114-2628-E-006-010 and NSTC 114-2218-E-006-021; and in part by the Higher Education Sprout Project, Ministry of Education, to the Headquarters of University Advancement at National Cheng Kung University (NCKU) .  \nNational Cheng Kung University ethics committee reviewed and approved all the study procedures (IRB Approval No. NCKU HREC-E-114-0870-2) .  \nThe authors are with the Department of Mechanical Engineering, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: [alireza.jafari.110@gmail.com](alireza.jafari.110@gmail.com); [sean901109@gmail.com](sean901109@gmail.com); [yliu@mail.ncku.edu.tw](yliu@mail.ncku.edu.tw)).  \nusing measurable variables. Subjective safety captures the human feeling of being safe and is evaluated through surveys. An objectively safe robot may still be subjectively unsafe and threatening if its movements appear erratic or violate social norms. Nevertheless, most research, such as [4], assumes pedestrians as dynamic objects and ignores the psychological component of safety.  \nSocial Force Models (SFM) are the primary model for predicting pedestrian movements in a crowd [5] . Extensions to micro-mobility vehicles like e-scooters [6]–[8] and Segways [9] are other emerging research directions. SFM is also a promising choice for real-time robot navigation [10], [11] because of its advantages, such as low computational cost, interpreta","cbCaiiZ06p2VOQJY","https://ap.wps.com/l/cbCaiiZ06p2VOQJY","pdf",1337899,3,1,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the paper address in mobile robot navigation around pedestrians?\",\"answer\":\"It addresses how mobile robots can ensure objective safety while also respecting pedestrians’ subjective safety and comfort during real-world walking interactions.\"},{\"question\":\"How do the proposed Predictive SFM variants incorporate future interaction information?\",\"answer\":\"They integrate predicted social-force vectors over a finite time horizon to form Predictive SFM (PSFM) and Predictive TSFM (PTSFM).\"},{\"question\":\"What were the main findings about objective and subjective safety improvements?\",\"answer\":\"PTTC integration improved objective safety metrics, while predictive contributions were limited and only sometimes visible in sub-metrics; subjective Likert ratings showed no significant differences in Mann-Whitney tests.\"}]",1784179122,20,{"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},"empirical-pedestrian-safety-assessment-in-a-mobile-robot-using-a-predictive-social-force-model","",{"@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/empirical-pedestrian-safety-assessment-in-a-mobile-robot-using-a-predictive-social-force-model/82245/",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","2026-07-22","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 the paper address in mobile robot navigation around pedestrians?","Question",{"text":74,"@type":75},"It addresses how mobile robots can ensure objective safety while also respecting pedestrians’ subjective safety and comfort during real-world walking interactions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do the proposed Predictive SFM variants incorporate future interaction information?",{"text":79,"@type":75},"They integrate predicted social-force vectors over a finite time horizon to form Predictive SFM (PSFM) and Predictive TSFM (PTSFM).",{"name":81,"@type":72,"acceptedAnswer":82},"What were the main findings about objective and subjective safety improvements?",{"text":83,"@type":75},"PTTC integration improved objective safety metrics, while predictive contributions were limited and only sometimes visible in sub-metrics; 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