## 1 Introduction  
With the advancement of science and technology, transportation tools have become integral to people's daily lives. While vehicles have brought great convenience, the issue of traffic safety in modern society is garnering increasing attention. Road traffic crashes cause nearly 1.3 million preventable deaths and an estimated 50 million injuries each year worldwide, making them the world's leading killer of children and young people. As predicted, traffic crashes will cause an additional 13 million deaths and 500 million injuries during the next decade and encumber the sustainable development of low- and middle-income countries [1]. These alarming statistics emphasize the need to find suitable methods to ensure road safety [2]. In recent years, the rapid development of technologies in intelligent connected vehicles (ICVs) [3] has led to a surge in the demand for autonomous driving in various industries. ICVs are expected to revolutionize transportation by enhancing safety, reducing pollution, and providing a more enjoyable travel experience [2]. At present, in order to improve the safety and comfort of intelligent vehicles, ICVs not only need to directly sense the environment and make decisions but also be able to cooperate and respond effectively, showcasing the advantages of multi-vehicle intelligence through vehicle-to-vehicle communication and coordination. However, compared with traditional cars, intelligent vehicles present numerous security challenges, such as network communication security [4], data security [5], driving safety [6], legal issues [7], etc. Consequently, an increasing number of scholars have been focusing on the road safety issues associated with ICVs. Their studies span a significant timeframe and encompass a wide range of topics, indicating the interdisciplinary nature of this field's development. This paper aims to conduct a visual bibliometric analysis of studies on the road safety issue of ICVs from 2013 to 2022. By collecting and analyzing existing research, the goal is to explore the current state of the art, research hotspots, and future research directions in this area, thus helping subsequent researchers to check existing results and fill vacancies. To sort out a systematic and comprehensive understanding of the subject matter in the jumble of existing research, traditional review methods are no longer sufficient. Traditional reviews heavily rely on qualitative research conducted by experts in the field based on their knowledge and understanding of the discipline, which can lead to omissions and subjective biases. Also, these review articles often follow a single thread and are limited in scope. For new researchers entering the discipline, it is difficult to accurately identify key documents amidst the extensive literature, leading to time-consuming and difficult reading, especially when trying to keep up with new research. Knowledge mapping, a bibliometric approach, on the other hand, uses technological means like data mining, which can effectively avoid the aforementioned problems and provide a more objective and accurate reflection of the history of the subject [8]. With the use of different software, knowledge mapping allows for comprehensive coverage of research on the related topic within a short timeframe, offering interdisciplinary and intuitive results. Some software even indicates research landmarks and predicts research trends. CiteSpace, developed in JAVA, is the most widely used visual analysis tool in bibliometrics. It offers a stable running environment and precise computational results. It incorporates various well-established algorithms that can perform cluster analysis and attribute computation, while its powerful visual editing functions enable researchers to customize map layouts, backgrounds, font colors, and scales as needed. Another useful tool is VOSviewer, which provides a specific demonstration of fixed data input patterns, including grid charts and density maps. The software requires minimal adjustments, primarily involving setting a threshold.