Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey

By Pranav Singh Chib, Pravendra Singh. IEEE Transactions on Intelligent Vehicles, 2023

310 citations recorded by the source corpus.

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 article 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. 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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Social Psychology

View Paper: https://www.semanticscholar.org/paper/8ac98f4ca139781a0b000c40fa6cdd2af7592b7f

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In Automotive Engineering

In Computer Vision and Pattern Recognition

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Citations in the corpus

DriveLM: Driving with Graph Visual Question Answering (2023, background): “Current Autonomous Driving (AD) stacks are still lacking crucial capabilities [8, 11].”