Autonomous Cars: Research Results, Issues, and Future Challenges

By Rasheed Hussain, Sherali Zeadally. IEEE Communications Surveys and Tutorials, 2019

541 citations recorded by the source corpus.

Throughout the last century, the automobile industry achieved remarkable milestones in manufacturing reliable, safe, and affordable vehicles. Because of significant recent advances in computation and communication technologies, autonomous cars are becoming a reality. Already autonomous car prototype models have covered millions of miles in test driving. Leading technical companies and car manufacturers have invested a staggering amount of resources in autonomous car technology, as they prepare for autonomous cars’ full commercialization in the coming years. However, to achieve this goal, several technical and nontechnical issues remain: software complexity, real-time data analytics, and testing and verification are among the greater technical challenges; and consumer stimulation, insurance management, and ethical/moral concerns rank high among the nontechnical issues. Tackling these challenges requires thoughtful solutions that satisfy consumers, industry, and governmental requirements, regulations, and policies. Thus, here we present a comprehensive review of state-of-the-art results for autonomous car technology. We discuss current issues that hinder autonomous cars’ development and deployment on a large scale. We also highlight autonomous car applications that will benefit consumers and many other sectors. Finally, to enable cost-effective, safe, and efficient autonomous cars, we discuss several challenges that must be addressed (and provide helpful suggestions for adoption) by designers, implementers, policymakers, regulatory organizations, and car manufacturers.

Subfields: Automotive Engineering, Electrical and Electronic Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/9146b928d5766e2f075db218b238ae2759326883

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Citations in the corpus

Blockchain for the Internet of Vehicles Towards Intelligent Transportation Systems: A Survey (2020, background): “physical systems (CPSs), vehicles are becoming smarter and more autonomous or semiautonomous than before [6]–[8].”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, unknown): “[219], [220]”
Explanations in Autonomous Driving: A Survey (2021, background): “drivers to be better than AVs in handling [51] unforeseen and uncharacteristic traffic situations.”
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, unknown): “All the surveys are categorized into several sub-sections, including the overall [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], localization [14], [15], [16], [17], [18], [19],”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “This scenario will not be practical for time-sensitive applications such as augmented reality, virtual reality [23], and autonomous vehicle network systems [24].”
Smart Transportation: An Overview of Technologies and Applications (2023, background): “…role of smart transportation in enhancing accessibility and mobility for disadvantaged groups (e.g., the aged, the disabled, and the low-income): Smart transportation may increase accessibility and mobility for underserved populations, including the elderly, disabled, and low-income people, [96].”