{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120114"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120114","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An end-to-end agent-map interaction framework for multi-agent trajectory prediction","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Li, Xiang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Driggs-Campbell, Katie"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Autonomous Driving","Machine Learning","Trajectory Prediction","Artificial Intelligence."],"languages":["en","eng"],"rights":["Copyright 2023 Xiang Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120114","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Driggs-Campbell, Katie"]},{"key":"dc:creator","label":"Author","values":["Li, Xiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-27"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autonomous Driving","Machine Learning","Trajectory Prediction","Artificial Intelligence."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Xiang Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120114"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Xiang Li, accepted the attached license on 2023-04-26 at 11:06.","The student, Xiang Li, submitted this Thesis for approval on 2023-04-26 at 11:17.","This Thesis was approved for publication on 2023-04-27 at 13:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19190 on 2023-09-01 at 16:55:36","This thesis proposes an end-to-end agent-map interaction framework for multi-agent trajectory prediction, which can be used in motion prediction tasks in relatively complex traffic scenarios that involve multiple agents, such as vehicles and pedestrians. The framework consists of an end-to-end deep learning model which takes motion history information of agents and rasterized context map image as input and predicts the future trajectories of all agents and occupancy on the map. The agents and map input features are extracted by capturing the interactions among agents, the temporal relationship of agents' motion, and the spatial information contained in the map. Agents and map embeddings interact with each other via a symmetric transformer structure based on cross-attention, then predictions of trajectories and occupancy are generated from post-interaction embeddings. The proposed framework does not utilize any extra internal levels of input representations such as lane graphs, motion heat maps, and waypoints; thus it is easier to be generalized for inputs with different modalities. The framework is implemented and then trained and evaluated on the nuScenes dataset. The framework is compared with many state-of-the-art works"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["An end-to-end agent-map interaction framework for multi-agent trajectory prediction"]}]}],"canonical_facts":{"dc:contributor":["Driggs-Campbell, Katie"],"dc:creator":["Li, Xiang"],"dc:date":["2023-05","2023-04-27"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Xiang Li, accepted the attached license on 2023-04-26 at 11:06.","The student, Xiang Li, submitted this Thesis for approval on 2023-04-26 at 11:17.","This Thesis was approved for publication on 2023-04-27 at 13:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19190 on 2023-09-01 at 16:55:36","This thesis proposes an end-to-end agent-map interaction framework for multi-agent trajectory prediction, which can be used in motion prediction tasks in relatively complex traffic scenarios that involve multiple agents, such as vehicles and pedestrians. The framework consists of an end-to-end deep learning model which takes motion history information of agents and rasterized context map image as input and predicts the future trajectories of all agents and occupancy on the map. The agents and map input features are extracted by capturing the interactions among agents, the temporal relationship of agents' motion, and the spatial information contained in the map. Agents and map embeddings interact with each other via a symmetric transformer structure based on cross-attention, then predictions of trajectories and occupancy are generated from post-interaction embeddings. The proposed framework does not utilize any extra internal levels of input representations such as lane graphs, motion heat maps, and waypoints; thus it is easier to be generalized for inputs with different modalities. The framework is implemented and then trained and evaluated on the nuScenes dataset. The framework is compared with many state-of-the-art works"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120114"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Xiang Li"],"dc:subject":["Autonomous Driving","Machine Learning","Trajectory Prediction","Artificial Intelligence."],"dc:title":["An end-to-end agent-map interaction framework for multi-agent trajectory prediction"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}