Vehicle-pedestrian interaction in partially observable environment

Deng, Zhaoux

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

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

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

Language

en

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

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