University of Illinois Urbana-Champaign

Vehicle-pedestrian interaction in partially observable environment

Deng, Zhaoux

Content Files
SP21-ECE499-Thesis-Deng, Zhaoxu.pdf

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https://hdl.handle.net/2142/110325

Description

Title
Vehicle-pedestrian interaction in partially observable environment
Author(s)
Deng, Zhaoux
Contributor(s)
Driggs-Campbell, Katherine
Issue Date
2021-05
Keyword(s)
  • reinforcement learning
  • autonomous driving
Date of Ingest
2021-08-12T18:12:30Z
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.
Type of Resource
text
Genre of Resource
dissertation/thesis
Language
en
Permalink
http://hdl.handle.net/2142/110325

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