VEHICLE-PEDESTRIAN INTERACTION IN PARTIALLY OBSERVABLE ENVIRONMENT
Deng, Zhaoxu
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https://hdl.handle.net/2142/124955
Description
Title
VEHICLE-PEDESTRIAN INTERACTION IN PARTIALLY OBSERVABLE ENVIRONMENT
Author(s)
Deng, Zhaoxu
Issue Date
2021-05-01
Keyword(s)
Reinforcement learning, autonomous driving
Date of Ingest
2024-10-24T12:13:48-05:00
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.
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