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Title:Autonomous vehicles that understand road agents: Detection, tracking, and behavior prediction
Author(s):Xu, Ke
Advisor(s):Driggs-Campbell, Katherine Rose
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):autonomous vehicles
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.
Issue Date:2020-05-12
Rights Information:Copyright 2020 Ke Xu
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05

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