{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124574"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124574","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Motion forecasting for autonomous driving in a streaming world","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Pang, Ziqi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Autonomous Driving","Motion Forecasting","Computer Vision"],"languages":["en","eng"],"rights":["Copyright 2024 Ziqi Pang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124574","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Yuxiong"]},{"key":"dc:creator","label":"Author","values":["Pang, Ziqi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-29"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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","Motion Forecasting","Computer Vision"]}]},{"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 2024 Ziqi Pang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124574"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Ziqi Pang, accepted the attached license on 2024-04-24 at 11:24.","The student, Ziqi Pang, submitted this Thesis for approval on 2024-04-24 at 11:32.","This Thesis was approved for publication on 2024-04-29 at 10:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20607 on 2024-09-16 at 00:44:30","Predicting the future and making decisions accordingly is essential for the safety of autonomous driving; thus, trajectory forecasting becomes a widely-studied problem for autonomous navigation by predicting the movements of traffic participants. Although the forecasting algorithms have advanced rapidly in recent years, we discover that the formulation of existing benchmarks has gaps with the real world and causes systematic failures of motion forecasting systems. Specifically, they evaluate forecasting based on independent snapshots of trajectories, which are not representative of real-world applications that operate on a continuous stream of data. To bridge this gap, this thesis begins by introducing a benchmark that champions the streaming formulation of real-world traffic for motion forecasting. Its most essential design is to continuously query future trajectories of other agents on streaming data, and we refer to it as \"streaming forecasting.\" Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks in autonomous driving. Moreover, forecasting in the context of continuous timestamps naturally asks for temporal coherence between predictions from adjacent timestamps. Based on this benchmark, this thesis further provides solutions and analysis for streaming forecasting to improve the safety of autonomous driving. We propose a plug-and-play meta-algorithm called \"Predictive Streamer\" that can adapt any snapshot-based forecaster into a streaming forecaster. Our algorithm estimates the states of occluded agents by propagating their positions with multi-modal trajectories containing multiple possible future states and leverages differentiable filters to ensure temporal consistency. Both occlusion reasoning and temporal coherence strategies significantly improve forecasting quality, resulting in 25% smaller endpoint errors for occluded agents and 10-20% smaller fluctuations of trajectories. Finally, this thesis is intended to generate interest within the community by highlighting the importance of addressing motion forecasting in its intrinsic streaming setting. Given the recent trend of end-to-end autonomous driving research, this thesis also contributes to developing forecasting algorithms and metrics under a streaming perception perspective."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Motion forecasting for autonomous driving in a streaming world"]}]}],"canonical_facts":{"dc:contributor":["Wang, Yuxiong"],"dc:creator":["Pang, Ziqi"],"dc:date":["2024-05","2024-04-29"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Ziqi Pang, accepted the attached license on 2024-04-24 at 11:24.","The student, Ziqi Pang, submitted this Thesis for approval on 2024-04-24 at 11:32.","This Thesis was approved for publication on 2024-04-29 at 10:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20607 on 2024-09-16 at 00:44:30","Predicting the future and making decisions accordingly is essential for the safety of autonomous driving; thus, trajectory forecasting becomes a widely-studied problem for autonomous navigation by predicting the movements of traffic participants. Although the forecasting algorithms have advanced rapidly in recent years, we discover that the formulation of existing benchmarks has gaps with the real world and causes systematic failures of motion forecasting systems. Specifically, they evaluate forecasting based on independent snapshots of trajectories, which are not representative of real-world applications that operate on a continuous stream of data. To bridge this gap, this thesis begins by introducing a benchmark that champions the streaming formulation of real-world traffic for motion forecasting. Its most essential design is to continuously query future trajectories of other agents on streaming data, and we refer to it as \"streaming forecasting.\" Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks in autonomous driving. Moreover, forecasting in the context of continuous timestamps naturally asks for temporal coherence between predictions from adjacent timestamps. Based on this benchmark, this thesis further provides solutions and analysis for streaming forecasting to improve the safety of autonomous driving. We propose a plug-and-play meta-algorithm called \"Predictive Streamer\" that can adapt any snapshot-based forecaster into a streaming forecaster. Our algorithm estimates the states of occluded agents by propagating their positions with multi-modal trajectories containing multiple possible future states and leverages differentiable filters to ensure temporal consistency. Both occlusion reasoning and temporal coherence strategies significantly improve forecasting quality, resulting in 25% smaller endpoint errors for occluded agents and 10-20% smaller fluctuations of trajectories. Finally, this thesis is intended to generate interest within the community by highlighting the importance of addressing motion forecasting in its intrinsic streaming setting. Given the recent trend of end-to-end autonomous driving research, this thesis also contributes to developing forecasting algorithms and metrics under a streaming perception perspective."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124574"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Ziqi Pang"],"dc:subject":["Autonomous Driving","Motion Forecasting","Computer Vision"],"dc:title":["Motion forecasting for autonomous driving in a streaming world"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}