Files in this item

FilesDescriptionFormat

application/pdf

application/pdfSP21-ECE499-Thesis-Deng, Zhaoxu.pdf (461kB)
(no description provided)PDF

Description

Title:Vehicle-pedestrian interaction in partially observable environment
Author(s):Deng, Zhaoux
Contributor(s):Driggs-Campbell, Katherine
Degree:B.S. (bachelor's)
Genre:Thesis
Subject(s):reinforcement learning
autonomous driving
Abstract:The dynamic nature of vehicles and pedestrians in urban environments poses a challenge for autonomous driving to safely make control decisions. We propose a reinforcement learning based motion-planning algorithm for the autonomous vehicle to interact with a partially observable environment where the states will be obtained by LSTM, to enable the autonomous vehicle’s ability to impute information from the environment with no direct sensing method. To verify this algorithm, we conduct parametric study and check the collision rate and time-to-complete (TTC), signifying the autonomous vehicle safely reaching the goal position without collision.
Issue Date:2021-05
Genre:Dissertation / Thesis
Type:Text
Language:English
URI:http://hdl.handle.net/2142/110325
Date Available in IDEALS:2021-08-12


This item appears in the following Collection(s)

Item Statistics