[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117300-en":3,"doc-seo-117300-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},117300,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Pedestrian Behavior Prediction Using Machine Learning Methods - Thesis for Degree of Doctor of Philosophy - Abstract","Accurate pedestrian behavior prediction reduces fatalities from pedestrian-vehicle collisions by helping automated vehicles interpret complex interactions. This PhD thesis predicts pedestrian motion and crossing intent using machine learning, with a focus on trajectory prediction, crossing intention prediction, and model transferability across scenarios and regions. Research gaps from prior literature are addressed through deep learning trajectory models on real-world data, enhanced by social and pedestrian-vehicle interactions and spectral features.","Thesis for The Degree of Doctor of Philosophy  \nPedestrian Behavior Prediction Using Machine Learning Methods  \nChi Zhang  \nDepartment of Computer Science and Engineering Division of Interaction Design and Software Engineering University of Gothenburg Gothenburg, Sweden, 2024  \nPedestrian Behavior Prediction Using Machine Learning Methods  \nChi Zhang  \n© Chi Zhang, 2024  \nexcept where otherwise stated.  \nAll rights reserved.  \nISBN 978-91-8069-817-7 (PRINT)  \nISBN 978-91-8069-818-4 (PDF)  \nDivision of Interaction Design and Software Engineering Department of Computer Science and Engineering  \nUniversity of Gothenburg SE-412 96 Gothenburg, Sweden  \nPhone: +46(0)31 772 1000  \nPrinted by Chalmers Reproservice, Gothenburg, Sweden 2024 .  \nTo my family.  \ni  \nPedestrian Behavior Prediction Using Machine Learning Methods  \nChi Zhang  \nDepartment of Computer Science and Engineering University of Gothenburg  \nAbstract  \nBackground: Accurate pedestrian behavior prediction is essential for reducing fatalities from pedestrian-vehicle collisions. Machine learning can support automated vehicles to better understand pedestrian behavior in complex scenarios.  \nObjectives: This thesis aims to predict pedestrian behavior using machine learning, focusing on trajectory prediction, crossing intention prediction, and model transferability.  \nMethods: We identified research gaps by reviewing the literature on pedestrian behavior prediction. To address these gaps, we proposed deep learning models for pedestrian trajectory prediction using real-world data, considering social and pedestrian-vehicle interactions. We integrated spectral features to improve model transferability. Additionally, we developed machine learning models to predict pedestrian crossing intentions using simulator data, analyzing interactions in both single and multi-vehicle scenarios. We also investigated cross-country behavioral differences and model transferability through a comparative study between Japan and Germany.  \nResults: For trajectory prediction, incorporating social and pedestrian-vehicle interactions into deep learning models improved accuracy and inference speed. Integrating spectral features using discrete Fourier transform improved motion pattern capture and model transferability. For crossing intention prediction, neural networks outperformed other machine learning methods. Key factors that influence pedestrian crossing behavior included the presence of zebra crossings, time to arrival, pedestrian waiting time, walking speed, and missed gaps. The cross-country study revealed both similarities and differences in pedestrian behavior between Japan and Germany, providing insights into model transferability.  \nConclusions: This thesis advances pedestrian behavior prediction and the understanding of pedestrian-vehicle interactions. It contributes to the development of smarter and safer automated driving systems.  \nKeywords: Pedestrian behavior, trajectory prediction, intention prediction, pedestrian-vehicle interaction, deep learning, machine learning  \nList of Publications  \nAppended publications  \nThis thesis is based on the following seven publications:  \n[I] Chi Zhang and Christian Berger, “Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review”  \nIEEE Transactions on Intelligent Transportation Systems, vol. 24, no.  \n10, pp. 10279-10301, 2023.  \n[II] Chi Zhang, Christian Berger, and Marco Dozza, “Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios”In proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), pp. 1515-1522. IEEE, 2021 .  \n[III] Chi Zhang and Christian Berger, “Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction”  \nIn proceedings of 2022 the 8th International Conference on Control, Automation and Robotics (ICCAR), pp. 230-236. IEEE, 2022 .  \n[IV] Chi Zhang, Zhongjun Ni, and Chris","cbCaibtlQxPVc1s6","https://ap.wps.com/l/cbCaibtlQxPVc1s6","pdf",4802895,1,76,"English","en",105,"# Abstract\n## Background, Objectives, Methods\n## Results, Conclusions\n# List of Publications\n## Appended publications\n## Other publications","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"Accurate pedestrian behavior prediction is essential to reduce pedestrian-vehicle collisions. The work supports automated vehicles in understanding pedestrians in complex scenarios.\"},{\"question\":\"Which prediction tasks are covered?\",\"answer\":\"The thesis focuses on pedestrian trajectory prediction and pedestrian crossing intention prediction. It also studies model transferability across different interaction setups and cross-country contexts.\"},{\"question\":\"How do social and interaction signals affect trajectory prediction?\",\"answer\":\"Incorporating social and pedestrian-vehicle interactions improves both prediction accuracy and inference speed in the deep learning trajectory models.\"}]",1785675064,192,{"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},"pedestrian-behavior-prediction-using-machine-learning-methods-thesis-for-degree-of-doctor-of-philosophy-abstract","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"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":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/pedestrian-behavior-prediction-using-machine-learning-methods-thesis-for-degree-of-doctor-of-philosophy-abstract/117300/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the thesis address?","Question",{"text":74,"@type":75},"Accurate pedestrian behavior prediction is essential to reduce pedestrian-vehicle collisions. The work supports automated vehicles in understanding pedestrians in complex scenarios.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which prediction tasks are covered?",{"text":79,"@type":75},"The thesis focuses on pedestrian trajectory prediction and pedestrian crossing intention prediction. It also studies model transferability across different interaction setups and cross-country contexts.",{"name":81,"@type":72,"acceptedAnswer":82},"How do social and interaction signals affect trajectory prediction?",{"text":83,"@type":75},"Incorporating social and pedestrian-vehicle interactions improves both prediction accuracy and inference speed in the deep learning trajectory models.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]