Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections

Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show that the trained model can effectively predict path curvature for various driving scenarios, providing a foundation for improved autonomous vehicle path planning systems. This work contributes to the advancement of machine learning applications in autonomous driving by demonstrating the effectiveness of end-to-end learning approaches for real time vehicle control applications.