Dynamic Path Planning for Autonomous Vehicles: A Neuro-Symbolic Approach

Fourth Author's Department

Computer Science & Engineering Department

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https://doi.org/10.5220/0012374700003636

All Authors

Omar Elrasas, 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

10.5220/0012374700003636

Abstract

The rise of autonomous vehicles has transformed transportation, promising safer and more efficient mobility. Dynamic path planning is crucial in autonomous driving, requiring real-time decisions for navigating complex environments. Traditional approaches, like rule-based methods or pure machine learning, have limitations in addressing these challenges. This paper explores integrating Neuro-Symbolic Artificial Intelligence (AI) for dynamic path planning in self-driving cars, creating two regression models with the Logic Tensor Networks (LTN) Neuro-Symbolic framework. Tested on the CARLA simulator, the project effectively followed road lanes, avoided obstacles, and adhered to speed limits. Root mean square deviation (RMSE) gauged the LTN models’ performance, revealing significant improvement, particularly with small datasets, showcasing Neuro-Symbolic AI’s data efficiency. However, LTN models had longer training times compared to linear and XGBoost regression models.

First Page

584

Last Page

591

Comments

Conference Paper. Record derived from SCOPUS.

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