2026-09-21T13:00:22Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/108028 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Driggs-Campbell, Katherine Rose Xu, Ke 2020-08-26T21:58:00Z 2020-08-26T21:58:00Z 2020-05-12 2020-05 Object detection, object tracking and behavior prediction are three fundamental problems towards human-level road agent understanding. In this thesis, we introduce a joint object detection and tracking model for real-time autonomous driving applications. Comparison with two state-of-the-art models on a research dataset shows that our model has the best detection performance and comparable tracking performance. We implement our algorithm on a real autonomous driving vehicle and conduct public road test to prove the robustness and reliability of our system. We further explore the task of vehicle behavior prediction for high-level understanding of road agents. We introduce the Fusion Seq2Seq model and compare it with two other baseline models. Experiments on a driver behavior dataset shows that our model can reasonably predict ego-vehicle actions. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms The student, Ke Xu, accepted the attached license on 2020-05-10 at 12:42. The student, Ke Xu, submitted this Thesis for approval on 2020-05-10 at 12:49. This Thesis was approved for publication on 2020-05-12 at 10:22. DSpace SAF Submission Ingestion Package generated from Vireo submission #15312 on 2020-08-25 at 17:13:44 Made available in DSpace on 2020-08-26T21:58:00Z (GMT). No. of bitstreams: 2 XU-THESIS-2020.pdf: 15706943 bytes, checksum: 360aec1cb71122e01babd17ff7300b08 (MD5) LICENSE.txt: 4202 bytes, checksum: 7d0958108ec049f2dd531889372b5c80 (MD5) Previous issue date: 2020-05-12 application/pdf http://hdl.handle.net/2142/108028 en Copyright 2020 Ke Xu autonomous vehicles detection tracking behavior prediction driver behavior Autonomous vehicles that understand road agents: Detection, tracking, and behavior prediction text Thesis Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.