Abstract

Autonomous vehicle navigation represents one of the most challenging applications of artificial intelligence, requiring real-time processing of multimodal sensor data and complex decision-making in dynamic environments. This paper presents novel deep neural network architectures specifically designed for autonomous vehicle navigation systems, integrating advanced computer vision, sensor fusion, and reinforcement learning techniques. Our proposed Multi-Modal Fusion Neural Network (MMFNN) combines convolutional neural networks for visual perception, recurrent neural networks for temporal sequence modeling, and attention mechanisms for dynamic feature selection. The architecture incorporates a hierarchical decision-making framework that processes LiDAR, camera, radar, and GPS data streams simultaneously to generate robust navigation decisions. Experimental evaluation using CARLA simulation environment and real-world driving datasets demonstrates significant improvements in navigation accuracy, obstacle avoidance, and path planning efficiency. The system achieves 96.8% obstacle detection accuracy, 0.15-meter average path deviation, and successful navigation completion in 94.2% of complex urban scenarios, outperforming existing state-of-the-art approaches by 12-18% across key performance metrics.