Knowledge-mapping visual analysis of formation research in connected and autonomous vehicles

Abstract

Connected and autonomous vehicle formation (CAVF) technology is considerably important for improving transportation efficiency, optimizing traffic flow, and reducing energy consumption. Despite the extensive research conducted on trajectory tracking control and other aspects of CAVF, the quality of the extant literature varies considerably, and research content remains scattered. To better promote the sustainable and healthy development of the CAVF field, this paper employs the mapping knowledge domain (MKD) methodology to comprehensively review and visualize the current research status in this domain. Based on this review, research themes, hotspots, research challenges, and future development directions are proposed. The findings suggest that the research on CAVF can be categorized into three primary developmental stages. China and the United States are the primary countries conducting CAVF research. There is a positive correlation between economic development and the generation of scientific research outcomes. Research institutions are predominantly concentrated in universities. The field exhibits significant interdisciplinary and integration characteristics, forming key research personnel and teams. It is expected that future research will concentrate on topics such as deep learning, trajectory optimization, energy management strategy, mixed vehicle platoon, and other related subjects. Research on cognition-driven intelligent formation decision-making mechanisms, resilience-oriented formation safety assurance systems, multiobjective collaborative formation optimization strategies, and digital twin-driven formation system validation platforms represents key future development directions.

Keywords

intelligent transportation system / connected and autonomous vehicles (CAV) / vehicle formation / mapping knowledge domain / visual analysis

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Funding

The National Natural Science Foundation of China(52302373)

The National Natural Science Foundation of China(52472317)

Natural Science Foundation of Beijing(L231023)

Beijing Nova Program(20230484443)