Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning

Karim Wahdan, Nourhan Ehab, Yasmin Mansy, Amr El Mougy

2024

Abstract

This paper focuses on local dynamic path planning for autonomous vehicles, using an Adaptive Reinforcement Learning Twin Delayed Deep Deterministic Policy Gradient (ARL TD3) model. This model effectively navigates complex and unpredictable scenarios by adapting to changing environments. Testing, using simulations, shows improved path planning over static models, enhancing decision-making, trajectory optimization, and control. Challenges such as vehicle configuration, environmental factors, and top speed require further refinement. The model’s adaptability could be enhanced by integrating more data and exploring a fusion between supervised reinforcement learning and adaptive reinforcement learning techniques. This work advances autonomous vehicle path planning by introducing an ARL TD3 model for real-time decision-making in complex environments.

Paper Citation

in Harvard Style

Wahdan K., Ehab N., Mansy Y. and El Mougy A. (2024). Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 272-279. DOI: 10.5220/0012363300003636

in Bibtex Style

@conference{icaart24,
author={Karim Wahdan and Nourhan Ehab and Yasmin Mansy and Amr El Mougy},
title={Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={272-279},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012363300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning
SN - 978-989-758-680-4
AU - Wahdan K.
AU - Ehab N.
AU - Mansy Y.
AU - El Mougy A.
PY - 2024
SP - 272
EP - 279
DO - 10.5220/0012363300003636
PB - SciTePress