Autonomous Vehicle Navigation Using Deep Learning

The development of automated vehicles (AVs) represents a leading technological objective of modern times, which aims to enhance safety while increasing system performance and reducing human driver participation. This research implements deep learning techniques through CNN obstacle detection while utilizing RL to handle real-time decision tasks in navigation control for autonomous vehicles. Engineering work comprises a simulation platform development for autonomous vehicles to navigate through complex environments through obstacle detection and traffic signal recognition and automated control implementation. Image datasets undergo analysis through a CNN component, which identifies environment objects within the system structure, while an RL agent employs real-world simulation operations in CARLA or Unity to generate navigation steps. The combined use of these components enables the system to perform effectively in environment sensing and function execution tasks. The model receives evaluation through standard metrics focused on F1 score representation, which provides optimal measures for object detection accuracy and classification precision. Experimental research in autonomous vehicle science advances by connecting deep learning technologies with simulated environment development to forecast transportation technological progress. The research reveals that deploying deep learning algorithms produces favourable results for raising operational efficiency and improving safety characteristics in self-driving vehicles.