DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning

DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning

Trajectory planning for automated vehicles in traffic has been a challenging task and a hot topic in recent research. The need for flexibility, transparency, interpretability and predictability poses challenges in deploying data-driven approaches in this safety-critical application. This paper propo...

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Bibliographic Details
Main Authors: Giovanni Lucente, Mikkel Skov Maarssoe, Sanath Himasekhar Konthala, Anas Abulehia, Reza Dariani, Julian Schindler
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Dynamic game
deep learning
generalized Nash equilibrium
LSTM
trajectory planning
Online Access:https://ieeexplore.ieee.org/document/10793110/
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https://ieeexplore.ieee.org/document/10793110/

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