{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116267"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116267","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Accurate detection for self driving cars using multi-resolution MIMO radar","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Ahmed, Waleed"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Al-Hassanieh, Haitham"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:56Z","subjects":["Radar Perception","Self-driving Cars","Object Detection","MIMO Radar","mmWave Sensing","Automotive Radar Dataset"],"languages":["en","eng"],"rights":["Copyright 2022 Waleed Ahmed"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116267","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Al-Hassanieh, Haitham"]},{"key":"dc:creator","label":"Author","values":["Ahmed, Waleed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-19"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Radar Perception","Self-driving Cars","Object Detection","MIMO Radar","mmWave Sensing","Automotive Radar Dataset"]}]},{"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 2022 Waleed Ahmed"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116267"]}]},{"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 2022-11-15 without embargo terms","The student, Waleed Ahmed, accepted the attached license on 2022-07-18 at 15:24.","The student, Waleed Ahmed, submitted this Thesis for approval on 2022-07-18 at 15:40.","This Thesis was approved for publication on 2022-07-19 at 08:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18369 on 2022-11-15 at 18:21:33","Millimeter wave (mmWave) radars are becoming a more popular sensing modality in self-driving cars due to their favorable characteristics in adverse weather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this thesis, we present Radatron, a system capable of accurate object detection using mmWave radar as a stand-alone sensor. To enable Radatron, we introduce a first-of-its-kind, high resolution automotive radar dataset collected with a cascaded MIMO (Multiple Input Multiple Output) radar. Our radar achieves 5cm range resolution and 1.2 degrees angular resolution, 10x finer than other publicly available datasets. We also develop a novel hybrid radar processing and deep learning approach to achieve high vehicle detection accuracy. We train and extensively evaluate Radatron to show it achieves 92.6% AP50 and 56.3% AP75 accuracy in 2D bounding box detection, an 8% and 15.9% improvement over prior art respectively."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Accurate detection for self driving cars using multi-resolution MIMO radar"]}]}],"canonical_facts":{"dc:contributor":["Al-Hassanieh, Haitham"],"dc:creator":["Ahmed, Waleed"],"dc:date":["2022-08","2022-07-19"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Waleed Ahmed, accepted the attached license on 2022-07-18 at 15:24.","The student, Waleed Ahmed, submitted this Thesis for approval on 2022-07-18 at 15:40.","This Thesis was approved for publication on 2022-07-19 at 08:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18369 on 2022-11-15 at 18:21:33","Millimeter wave (mmWave) radars are becoming a more popular sensing modality in self-driving cars due to their favorable characteristics in adverse weather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this thesis, we present Radatron, a system capable of accurate object detection using mmWave radar as a stand-alone sensor. To enable Radatron, we introduce a first-of-its-kind, high resolution automotive radar dataset collected with a cascaded MIMO (Multiple Input Multiple Output) radar. Our radar achieves 5cm range resolution and 1.2 degrees angular resolution, 10x finer than other publicly available datasets. We also develop a novel hybrid radar processing and deep learning approach to achieve high vehicle detection accuracy. We train and extensively evaluate Radatron to show it achieves 92.6% AP50 and 56.3% AP75 accuracy in 2D bounding box detection, an 8% and 15.9% improvement over prior art respectively."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116267"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Waleed Ahmed"],"dc:subject":["Radar Perception","Self-driving Cars","Object Detection","MIMO Radar","mmWave Sensing","Automotive Radar Dataset"],"dc:title":["Accurate detection for self driving cars using multi-resolution MIMO radar"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:24:56Z"}