End-to-end Autonomous Driving: Advancements and Challenges

End-to-end autonomous driving methodologies eliminate the need for manually defined rules and explicit module interfaces. Instead, these approaches directly map trajectory points or control signals from raw sensor data, thereby addressing the inherent shortcomings associated with traditional modular methods, such as information loss and cascading errors, and overcoming the performance limitations imposed by rule-driven frameworks. Recent advancements in self-supervised-learning-based generative artificial intelligence have exhibited substantial emergent intelligence capabilities, significantly promoting the evolution of end-to-cnd methodologies. However, the existing literature lacks a comprehensive synthesis of the advancements in generative end-to-end autonomous driving. Consequently, this paper systematically reviews the research progress, technical challenges, and developmental trends in end-to-end autonomous driving. Initially, the input and output modalities of the end-to-end models are delineated. Based on the historical progression of end-to-end autonomous driving, this paper provides an overview and comparative analysis of the foundational concepts, current research status, and technical challenges of traditional, modular, and generative end-to-end methods. Subsequently, the evaluation methodologies and training datasets utilized for end-to-end models are summarized. Furthermore, this paper explores the challenges currently faced by end-to-end autonomous driving technologies in relation to generalization, interpretability, causality, safety, and comfort. Finally, predictions are made for the future trends of end-to-end autonomous driving, emphasizing the fact that edge scenarios provide critical support for the training of end-to-end models, which can enhance the generalization capabilities. In addition, self-supervised learning can effectively improve training efficiency, personalized driving can optimize user experience, and world models represent a pivotal direction for the further advancement of end-to-end autonomous driving. The findings of this research serve as a significant reference for refining the theoretical framework and enhancing the performance of end-to-end autonomous driving systems. © 2024 Chang'an University. All rights reserved.