Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning for Autonomous Vehicle Signal Processing and Interpretation

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  • Dr. Andrés Páez-Gaviria
Dr. Andrés Páez-Gaviria
Professor of Industrial Engineering, Universidad EIA (Colombia)
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Published 23-06-2024

Keywords

  • Autonomous Vehicle,
  • Environmental Perception
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Andrés Páez-Gaviria, “Deep Learning for Autonomous Vehicle Signal Processing and Interpretation”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–13, Jun. 2024, Accessed: Nov. 25, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/33

Abstract

The evolution of the techniques developed for these purposes allows the construction of systems with increasing levels of automation intended for automotive transport. In particular, the advances in deep learning, a technique also known as deep artificial neural networks, can be considered as one of the great milestones in the construction of these systems. Despite the advancement, there is little material aimed at providing a general view of the processing that must be applied, as well as the learning techniques that are used in one of these systems to perform the tasks necessary for automatic operation. Therefore, this chapter has the objective of presenting these techniques in a systematic way, so that they are more easily used in different situations of an autonomous system and can then be combined to perform complex tasks related to the automatic operation of a vehicle.

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