## 3 Related Work/Literature Studies
They highlight the benefits of these systems and note that they are now standard [31; 32], contributing to improved vehicle reliability [33; 34; 35; 36]. In the past decade, there has been a notable increase in publications focused on telematics tools in road freight transport [37], including their integration with IoT networks and telecommunication systems [38; 39]. An interesting conclusion presented in publication [40] suggests that a truck is considered unmonitored if its diagnostics rely solely on information about the condition of its systems. The analysis suggests that most research focuses on optimizing the number of sensors used and rationalizing their placement [41; 42]. Various research approaches have been proposed, including empirical studies [31; 43], graph theory [44], and decision-making algorithms in conditions of informational uncertainty [45]. Interesting results are reported in publications [46; 47], where fault diagnosis based on Bayesian Networks (BNs) is proposed. Procedures for modeling, identifying, and diagnosing faults, validating and verifying the results obtained, and modeling BN structure and parameters have also been examined. The literature analysis indicates the following:  
1. The diagnostic systems discussed in the publications studied align with the classification presented in Figure 4.
2. Recent publications primarily address the modeling of fault dynamics, evaluating their characteristics, and the decision-making processes of drivers [25; 48; 49]. Key areas of focus include: 1. Machine Learning, which is the leading approach utilized in contemporary in-vehicle telematics devices for trucks; 2. The fault identification techniques used can be grouped as follows: * Techniques for identifying operating parameters based on modeling changes in their values and verifying the results. They help in identifying faults based on established patterns and anticipated responses, focusing on detecting faults by recognizing multiplicative effects. * Techniques for clustering individual subsystems and components of the transport vehicle include adapting a network of monitoring sensors to identify faults. By analyzing sensor data, deviations from normal behavior can be flagged as potential faults. These techniques are particularly suited for identifying individual faults [50]. * Adaptive fault detection techniques are based on a combination of the above two techniques. * Techniques based on predictive algorithms that analyze statistical data include learning to identify relationships between different operational parameters. They aid in predicting fault progression and are oriented toward detecting faults by identifying additive effects [51]. The key distinction among the discussed approaches is the necessity for prior knowledge of the faults that need to be detected [34; 52]. The first three methods rely on certain assumptions for effective and reliable fault identification, such as the faults being quasi-stable or changing slowly over time. In contrast, the authors propose that effective fault detection systems should be designed to diagnose both occasional and surprising threats, map existing faults, and evaluate the probability of both new faults and multiplicative effects. Furthermore, such a system should provide multi-scenario preventive measures and include a feature for real-time updates on the STT's operational status through a visual representation on a 3D monitor. Given the rapid developments in this field, the authors have decided to use for comparative analysis only specialized publications dated before 2014 (Table 6).