End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems, such as their overwhelming complexity and propensity for error propagation. Autonomous driving transcends conventional traffic patterns by proactively recognizing critical events in advance, ensuring passengers' safety and providing them with comfortable transportation, particularly in highly stochastic and variable traffic settings. This paper presents a comprehensive review of the End-to-End autonomous driving stack. It provides a taxonomy of automated driving tasks wherein neural networks have been employed in an End-to-End manner, encompassing the entire driving process from perception to control, while addressing key challenges encountered in real-world applications. Recent developments in End-to-End autonomous driving are analyzed, and research is categorized based on underlying principles, methodologies, and core functionality. These categories encompass sensorial input, main and auxiliary output, learning approaches ranging from imitation to reinforcement learning, and model evaluation techniques. The survey incorporates a detailed discussion of the explainability and safety aspects. Furthermore, it assesses the state-of-the-art, identifies challenges, and explores future possibilities. We maintained the latest advancements and their corresponding open-source implementations at https://github.com/Pranav-chib/Recent-Advancements-in-End-to-End-Autonomous-Driving-using-Deep-Learning.

翻译：端到端驾驶是一种有前景的范式，因为它规避了模块化系统的缺点，例如其过度的复杂性和误差传播倾向。自动驾驶通过提前主动识别关键事件，超越了传统的交通模式，确保乘客安全并提供舒适的出行体验，尤其是在高度随机和多变的交通环境中。本文对端到端自动驾驶技术栈进行了全面综述。它提出了一种自动驾驶任务的分类法，其中神经网络以端到端方式被应用，涵盖了从感知到控制的整个驾驶过程，同时解决了实际应用中遇到的关键挑战。分析了端到端自动驾驶的最新进展，并根据基本原理、方法论和核心功能对研究进行了分类。这些类别涵盖了传感器输入、主要和辅助输出、从模仿学习到强化学习的学习方法，以及模型评估技术。该综述包含了对可解释性和安全性方面的详细讨论。此外，它评估了当前技术水平，识别了挑战，并探讨了未来可能性。我们在 https://github.com/Pranav-chib/Recent-Advancements-in-End-to-End-Autonomous-Driving-using-Deep-Learning 上维护了最新进展及其相应的开源实现。