Meta-Analysis and Review of Artificial Intelligence (AI) and Deep Learning Algorithms on Autonomous Vehicles (Avs) Via Vision-Based System: Current Trends, Issues, and Future Direction
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The invention of autonomous vehicles (AVs) and their use in transportation have been substantially accelerated by technological developments in artificial intelligence (AI) and deep learning Algorithms. Vision-based systems are a crucial part of AVs for detecting their surroundings and making the right decisions. At the same time, they are in motion, thanks to massive data from numerous sensor devices and sophisticated computing power. They understand how AI and deep learning functions in AV systems are crucial in achieving the objective of full automation, or self-driving, systems. Previous studies have done a fantastic job of looking into various facets of using AI and deep learning in AV production. Nevertheless, few studies have provided a comprehensive analysis of existing methods for integrating AI in AVs to the research community. This paper offers a systematic review of the most important papers in this field of research. It seeks to close the knowledge gap by providing state-of-the-art practices, challenges, and future direction. Its specific goal is to examine the various algorithms, models, and techniques applied to AVs by enhancing AI and deep learning for effective vision, navigation, and location in making decisions. It looks into the methods now in use to determine the potential applications of AI and the difficulties and problems that come with putting them into practice. This study offers more insights into possible opportunities for utilizing AI and deep learning in conjunction with other developing technologies, based on an examination of current practices and technological advancements. Big data, high computing power, and high-resolution navigation; expanded simulation platforms through a vision-based system.
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