How to Cite
Abstract
Autonomous vehicles (AVs) are at the forefront of the automotive industry. This is part of global technological advancements with the ultimate goal of commercialization as modern transportation infrastructures move toward complete automation [1]. Artificial intelligence (AI) platforms include several different functionalities, such as machine learning (ML) or deep learning (DL) algorithms, navigation sensing systems, and others. ML techniques are used to analyze and decide on different situations detected by AVs' onboard sensors. Expert systems with specialized knowledge learned and imparted by diverse ML and DL cybersecurity models monitor the navigation sensory data created by sensors and decide on the real-time conditions of the travel.[2] Autonomous vehicles carry out the driving task (DT) by sensory data resembling human sensory perception. The DT elements of AVs can be categorized into four major components: perception, event prediction, data processing, and route planning. Most ML and DL models for AVs are related to perception systems where changes in real-time driving conditions and the surrounding environment are detected. In order to process all of these functionalities, we need a pertinent processing unit with storage capacities and a high processing rate.
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