Authors:
Omar Elrasas
1
;
Nourhan Ehab
1
;
Yasmin Mansy
1
and
Amr El Mougy
2
Affiliations:
1
Department of Computer Science and Engineering, German University in Cairo, Cairo, Egypt
;
2
Department of Computer Science and Engineering, American University in Cairo, Cairo, Egypt
Keyword(s):
Neuro-Symbolic AI, Dynamic Path Planning, Autonomous Vehicles.
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.