A Comprehensive Scoping Review of Autonomous Vehicle Models: An In-depth Analysis of Emerging Trends, Challenges, and Future Directions

Abstract

Self-driving vehicles (SDVs), also known as autonomous vehicles (AVs), are poised to revolutionize transportation by operating independently through the integration of machine learning algorithms, advanced processing units, and sensor networks. Many organizations around the world are developing their own SDV models, and for this purpose, this paper aims to identify emerging trends and patterns in SDV development by conducting a systematic scoping review (SSR). The research involved the selection of 85 relevant papers from an initial pool of 551 entries across multiple academic databases, using well-defined inclusion and exclusion criteria along with snowballing techniques. The results highlight the critical technical specifications necessary for both full-scale and miniature SDV models, emphasizing key software and hardware architectures, essential sensors, and their primary suppliers. Additionally, the analysis examines publication trends, including publisher and venue distribution, authors' affiliations, and the most active countries in SDV research. This work can guide researchers in designing their SDV models, identifying key challenges, and exploring opportunities that are expected to influence future research and development in autonomous vehicle technology.