  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.
