Dynamic Path Planning for Autonomous Vehicles Using Adaptive Reinforcement Learning

Fourth Author's Department

Computer Science & Engineering Department

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https://doi.org/10.5220/0012363300003636

All Authors

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

Document Type

Research Article

Publication Title

International Conference on Agents and Artificial Intelligence

Publication Date

1-1-2024

doi

10.5220/0012363300003636

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.

First Page

272

Last Page

279

Comments

Conference Paper. Record derived from SCOPUS.

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