Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Practical Self-Driving Cars: Survey of the State-of-the-Art

Version 1 : Received: 7 February 2022 / Approved: 8 February 2022 / Online: 8 February 2022 (15:42:51 CET)
Version 2 : Received: 4 October 2023 / Approved: 6 October 2023 / Online: 10 October 2023 (12:28:06 CEST)

How to cite: Saha, D.; De, S. Practical Self-Driving Cars: Survey of the State-of-the-Art. Preprints 2022, 2022020123. Saha, D.; De, S. Practical Self-Driving Cars: Survey of the State-of-the-Art. Preprints 2022, 2022020123.


Self-Driving Vehicles or Autonomous Driving (AD) have emerged as the prime field of research in Artificial Intelligence and Machine Learning of late. The indicated market share of existing vehicles might be supplanted by these self-driving vehicles within the next few decades. While AD may appear to be relatively easy, in fact, it is quite the contrary owing to involvement and coordination amongst various kinds of systems. Numerous research studies are being conducted at various stages of these AD systems. While some find the various stages of the AD Pipeline beneficial, others tend to rely on Computer Vision mostly. This paper attempts to summarise the recent developments in Autonomous Vehicle architecture. Although some people might seem to be sceptical about the pragmatic use of AD as an alternative to existing vehicles, the plethora of research and experiments being conducted suggests the opposite. Indeed, there are many challenges to implementing AD in the real world, but significant progress made in the last couple of years indicates general acceptance of AD in upcoming years.


cnn; ann; robotics; machine learning; ann; artificial intelligence


Computer Science and Mathematics, Robotics

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