## 3 Methodology
### Analytic Process
Specifically, 30 countries with the highest number of publications were analyzed, revealing three main clusters of collaborative relationships. These clusters are not exclusive, as evidenced by significant cross-cluster collaborations, such as those between the USA and Germany, Italy and Germany, and France and Germany. This analysis also highlights the prominent role of the USA in global research collaboration and the strong collaborative ties between Germany and Austria. In terms of publication venues, a wide variety of sources were identified, with IEEE Access (published by IEEE) being the most frequently used journal, hosting 18 articles. This is followed by Lecture Notes in Computer Science (published by Springer) with 12 publications, and the ACM International Conference Proceeding Series with 9 articles. When examining citations, the top three sources are Lecture Notes in Computer Science (Springer), which received 219 citations, IEEE Transactions on Intelligent Transportation Systems with 126 citations, and IEEE Access with 119 citations. Key terms frequently appearing in the literature include \"cybersecurity\" (and its variants such as \"security\" and \"cyber security\"), and \"automotive\" (including \"automotive security\" and \"cyber security\"). Secondary keywords include \"cyber-physical systems\" (CPS), \"embedded systems\", \"Internet of Things\" (IoT), \"connected vehicles\", \"VANET\" (vehicular ad-hoc networks), \"cloud computing\", and \"intelligent transportation systems\" (ITS). Further, the literature addresses topics related to autonomous driving, including \"autonomous vehicles\" and \"machine learning\", as well as terms associated with functional safety and security, such as \"privacy\", \"intrusion detection\", \"in-vehicle security\" (including Controlled Area Network systems), and relevant standards (such as ISO 26262 [58] or ISO/SAE 21434 [34]). In our analytical process, the second step involved text processing, where we handled the titles and abstracts extracted from the 517 papers we downloaded, following the methodology outlined by [PERSON] et al. [21]. Initially, we removed the dataset of any duplicate entries. Subsequently, non-alphanumeric characters were removed from the documents. The documents were then tokenized into individual words, specifically utilizing uni-grams. We opted for uni-grams because of their enhanced flexibility and ability to yield a more concise vocabulary size compared to bi-grams or tri-grams. All words were standardized to lowercase to ensure uniformity. Additionally, we eliminated stop words (such as \"and\", \"or\", and \"the\"), which lack meaningful information, and excluded words containing fewer than three characters. All of these steps provide us with a cleaner dataset of words to work with, which improves the interpretability of the topics and reduces the computational burden. These data are put into a document-term matrix, where the rows correspond to the documents and the columns the different tokens. In this matrix, each entry indicates the frequency of appearance of the token in the document. Since the resulting matrix is very large and sparse, following the approach outlined by [59], we opt to reduce the size of our matrix. Specifically, we retain only those tokens whose frequency score is higher than the median of all scores. By employing this method, we preserve tokens that have a significant presence and uniquely identify the texts, allowing us to focus on the most relevant and distinctive elements within these data. This reduction not only streamlines the dataset but also enhances the efficiency and effectiveness of our analysis, enabling us to concentrate on the most meaningful aspects of the texts in our study. While the use of the median threshold allowed us to streamline the dataset, we acknowledge that a more sophisticated threshold based on the distribution of token frequencies could potentially enhance the analysis by better capturing the variability in token usage.