Autonomous Vehicle Navigation with Deep Learning: A Comprehensive Review

Abstract

AVs are a revolutionary technology in the intelligent transportation industry integrating a sophisticated sensing, computing, and controlling system to facilitate safe and effective self-driving. At the heart of this development lies deep learning (DL) that has become the foundation of perception, decision-making, and navigation on complex and dynamic driving environments. As opposed to classical rule-based algorithms, DL models can learn hierarchical representations using large volumes of sensor and traffic data and achieve major gains in object-detection, lane-recognition, obstacle-avoidance, and route-planning tasks. In this paper, I have reviewed deep learning methods in autonomous vehicle navigation in detail. It covers a few of the more well-known architectures such as Convolutional Neural Networks (CNNs), visual perception; Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) to make sequential decisions; and deep models based on Reinforcement Learning (RL), adaptive navigation strategies. Furthermore, other methods that might be employed to improve environmental knowledge are discussed, including the application of multimodal fusion technologies, which integrate LiDAR, radar, and vision cameras. The article talks about real-world application, benchmark datasets, and simulation environments that facilitate DL-based research on AV. Even after an accelerated development, explainability, robustness in adverse weather, real-time computational efficiency and ethical considerations of safety-critical decisions continue to be challenges. Lightweight DL systems, federated learning of collaborative AVs, and explainable AI systems are the next steps to control regulatory compliance and user trust. This review combines progress, issues, and opportunities to emphasize the revolution in deep learning in the field of autonomous vehicle navigation and find ways to enable sustainable, reliable, and large-scale implementation.