A Comprehensive Systematic Scoping Review of Self-Driving Vehicle Models

Abstract

Self-driving vehicles (SDVs), also known as autonomous vehicles (AVs), are anticipated to revolutionize transportation by operating independently through the integration of machine learning algorithms, advanced processing units, and sensor networks. Numerous organizations globally are actively developing SDV models, prompting this paper’s objective to identify emerging trends and patterns in SDV development through a comprehensive systematic scoping review (SSR). This research involved selecting 85 relevant studies from an initial set of 551 records across multiple academic databases, utilizing well-defined inclusion and exclusion criteria along with snowballing techniques to ensure a thorough analysis. The findings emphasize critical technical specifications required for both full-scale and miniature SDV models, focusing on key software and hardware architectures, essential sensors, and primary suppliers. Additionally, the analysis explores publication trends, including publisher and venue distribution, authors’ affiliations, and the most active countries in SDV research. This work aims to guide researchers in designing their SDV models by identifying key challenges and exploring opportunities likely to shape future research and development in autonomous vehicle technology.