## 3 Research Methodology
### Synthesis  
As reported in Table 2, only a few works have considered the security concerns in the proposed work. [PERSON] et al. [24] conducted an informal analysis of possible points of failure in a typical ACN system, highlighting the problems related to smartphone applications due to filters used for preventing false-positive detection. In [25], the authors performed an evaluation of the availability and performance of the proposed architecture; their results showed a reliable network, independently from the number of vehicles considered, but a decreasing performance when vehicle speed increased. The work analyzed in [26] confirmed the poor availability of full smartphone-based systems, where if the phone battery is dead, it is impossible to notify emergency services.  
Taking a look at the other works, which do not exhibit any security evaluations, it is possible to notice that almost all the systems integrate GPS, which is one of the most important components in the ACN system, while many connections, such as cellular, Bluetooth (BT), and WiFi, can be applied depending on the architecture. Regarding the external services, only a few of them leverage the machine learning (ML) model, while ERS is included in the majority of works. The communication with the final user is usually implemented using a mobile or car application, leveraging infotainment systems.  
Considering the intricate nature of these systems and the considerations made in some of these works, it appears evident that multiple methods must be employed for communication and sensing, and that these methods cannot be restricted to a single approach. While machine learning (ML) can play a crucial role, it is not the sole determinant of accurate detection, as demonstrated by the majority of studies. GPS, on the other hand, stands out as one of the most critical components, capable of significantly reducing the response time of emergency services. Despite the existence of several works advocating a similar reliable and comprehensive architecture, the authors chose to focus on the works of [PERSON] et al. [27] and [PERSON] et al. [28] due to the following considerations:  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\  & & & & & & & & &  
* The dissimilarity between these two architectures primarily lies in the diversity of connectivity options they offer. While one architecture presents a multitude of methods, the other exclusively relies on WiFi. Our focus centers on assessing the ramifications of employing a singular communication method in contrast to an architecture that incorporates a variety of means.
* While alternative architectures may exhibit similar disparities in connectivity, these two stand out as the exclusive implementations integrating a camera system alongside diverse connectivity. This amalgamation represents a novel direction in ACN systems, necessitating thorough consideration and mitigation of privacy concerns.
* A direct comparison of a non-ML-based system with an ML-based system can be achieved by examining these two architectures. This comparative endeavor is driven by the aspiration to contribute valuable insights into the comparative efficacy and performance characteristics of these divergent technological approaches.  
Although there are some differences in communication means and some similarities in their architecture components, the possible attacks on components can vary considering the data flows adopted, as shown in the following sections. In what follows, we use the name of the first author to refer to them.