Autonomous vehicles that understand road agents: Detection, tracking, and behavior prediction

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Xu, Ke

Contributors dc:contributor

• Driggs-Campbell, Katherine Rose

• autonomous vehicles
• detection
• tracking
• behavior prediction
• driver behavior

Statement dc:rights

• Copyright 2020 Ke Xu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108028

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108028