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Review

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

by
Divya Garikapati
1,*,† and
Sneha Sudhir Shetiya
2,†
1
Institute of Electrical and Electronics Engineers (IEEE), New York, NY 10016, USA
2
Torc Robotics, Inc., Blacksburg, VA 24060, USA
*
Author to whom correspondence should be addressed.
First Author is a Senior IEEE Member, Second Author is a Staff Software Engineer and a Senior IEEE Member.
Big Data Cogn. Comput. 2024, 8(4), 42; https://doi.org/10.3390/bdcc8040042
Submission received: 25 March 2024 / Revised: 28 March 2024 / Accepted: 2 April 2024 / Published: 7 April 2024
(This article belongs to the Special Issue Deep Network Learning and Its Applications)

Abstract

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.
Keywords: artificial intelligence (AI); Machine learning (ML); deep learning (DL); deep neural networks (DNNs); natural language processing (NLP); autonomous vehicles (AVs); safety; security; ethics; emerging trends; trucks vs. cars; autonomy levels; operational design domain (ODD); software-defined vehicles (SDVs); connected and automated vehicles (CAVs); in-vehicle AI assistant; internet of things (IoT); generative AI (GenAI) artificial intelligence (AI); Machine learning (ML); deep learning (DL); deep neural networks (DNNs); natural language processing (NLP); autonomous vehicles (AVs); safety; security; ethics; emerging trends; trucks vs. cars; autonomy levels; operational design domain (ODD); software-defined vehicles (SDVs); connected and automated vehicles (CAVs); in-vehicle AI assistant; internet of things (IoT); generative AI (GenAI)

Share and Cite

MDPI and ACS Style

Garikapati, D.; Shetiya, S.S. Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data Cogn. Comput. 2024, 8, 42. https://doi.org/10.3390/bdcc8040042

AMA Style

Garikapati D, Shetiya SS. Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data and Cognitive Computing. 2024; 8(4):42. https://doi.org/10.3390/bdcc8040042

Chicago/Turabian Style

Garikapati, Divya, and Sneha Sudhir Shetiya. 2024. "Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape" Big Data and Cognitive Computing 8, no. 4: 42. https://doi.org/10.3390/bdcc8040042

APA Style

Garikapati, D., & Shetiya, S. S. (2024). Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data and Cognitive Computing, 8(4), 42. https://doi.org/10.3390/bdcc8040042

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