End-to-End Autonomous Driving: From Classic Paradigm to Large Model Empowerment—A Comprehensive Survey

In recent years, autonomous driving technology has witnessed rapid global development, offering effective solutions to increasingly severe challenges related to traffic congestion and road safety. Among various paradigms, end-to-end autonomous driving has emerged as a promising alternative to traditional modular systems, owing to its streamlined architecture, enhanced decision consistency, and superior generalization capabilities. This survey provides a comprehensive review of the evolution and core technologies of end-to-end autonomous driving. It emphasizes the applications and developments of imitation learning (IL), reinforcement learning (RL), and other paradigms. Furthermore, it highlights the emerging paradigm empowered by foundation models, such as large language models (LLMs) and vision-language models (VLMs), and systematically categorizes recent advances in planning, reasoning, data generation, and scene understanding. In light of persistent challenges such as multi-modal fusion complexity, low sample efficiency, and safety risks, this survey summarizes representative research efforts and corresponding solutions. Finally, it outlines future directions, including the integration of world models (WMs) for unified data generation and inference optimization, the advancement of foundation model architectures toward modular design, sparse activation mechanisms, and knowledge distillation, and the realization of deployable, transferable, and unified multi-modal frameworks. This survey aims to serve as a comprehensive theoretical and technical reference for researchers in end-to-end autonomous driving, facilitating its evolution toward higher performance, stronger generalization, and enhanced safety assurance.