Autonomous vehicles that understand road agents: Detection, tracking, and behavior prediction
Xu, Ke
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https://hdl.handle.net/2142/108028
Description
Title
Autonomous vehicles that understand road agents: Detection, tracking, and behavior prediction
Author(s)
Xu, Ke
Issue Date
2020-05-12
Director of Research (if dissertation) or Advisor (if thesis)
Driggs-Campbell, Katherine Rose
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
autonomous vehicles
detection
tracking
behavior prediction
driver behavior
Abstract
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.
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