Vol. 3 No. 1 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning for Autonomous Vehicle Signal Recognition and Interpretation

  • Dr. Kevin Whalen
Dr. Kevin Whalen
Associate Professor of Cybersecurity, Purdue University (Branch outside normal colleges)
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Published 30-06-2023

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How to Cite

[1]
Dr. Kevin Whalen, “Deep Learning for Autonomous Vehicle Signal Recognition and Interpretation”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 196–215, Jun. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/88

Abstract

In particular, Deep Learning architectures have been successfully implemented in different classification tasks. A key element for the realization of infrastructure and vehicle classification systems is the ability to accurately classify the messages’ contents in presence of various types of signal degradation. The various types of possible environment degradations may include strong background noise (e.g., a busy intersection with many vehicles and people; a noisy motorway), simulation of rain deposits on the camera sensor; and different forms of occlusions interfering with some or all relevant regions of the signals. All of these factors can contribute to a remarkable decrease in performance of recognition methodologies that are not specifically optimized with a view to robustness and minimal computational demand.

[1] [2]The development of technologies for autonomous driving has gained considerable interest from academics and industry, motivated by potential applications to the transportation system. Classification of Optical, Acoustic, and Lidar Signals for the Recognition and Interpretation of Safety Messages and Traffic Signs: Recently, the usage of Artificial Intelligence (AI) in autonomous vehicles has increased. This is because it is designed and developed with robustness and simplicity in mind. Specifically, Deep Learning (DL) has the potential to perform better and faster than its conventional counterparts. This has made Deep Learning techniques the main candidate in various tasks on both infrastructure and in-vehicle sensors.

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