## 2 An Overview of Published Articles
The results reveal that the \"jack-knifing\" phenomenon is attributed to the lateral instability of the system when the sliding speed exceeds 15 km/h, accompanied by lateral slip conditions. The conclusion of this study may provide a theoretical basis and guidance for the safe application of the new taxi-out departure mode. In their article, [PERSON] et al. (contribution 10) investigated the potential impacts of speeding violations in the United States, encompassing the top ten states with the highest crash rates. Several variables associated with drivers, surroundings, vehicles, roads, and weather were investigated. Three different machine learning algorithms were used to predict speeding violations. The research results revealed that age, accident year, road alignment, weather conditions, the time of the accident, and speed limits are the key factors. Understanding how these factors affect speeding violations helps decision-makers devise strategies to reduce these violations and enhance road safety. In the eleventh article, [PERSON] et al. (contribution 11) present a novel method aimed at assessing the sensitivity of autonomous vehicles to logical situations and events before their use to generate specific scenarios for simulators. They proposed a characterization of the inputs used in sensitivity analysis and an approach to establishing a distribution function that facilitates the selection of situations and events based on their importance in terms of sensitivity. [PERSON] et al. (contribution 12) conclude that determining the priority of the forces that act on vehicles transporting wooden logs is crucial for accurately modeling the transport parameters of this cargo. The authors of the article use an analytical survey, an expert survey, and its analysis, as well as various methods to determine correlations. The research results revealed that the main forces that affect vehicles transporting logs manifest in the following order of priority: inertial forces, gravity forces, frictional forces, and centrifugal forces. Innovative pre-crash systems are expected to contribute to mitigating unavoidable accidents. However, there are no standardized testing methods for pre-crash systems. In their work, [PERSON] et al. (contribution 13) propose a novel validation and safety assessment strategy for a perception-based crash severity prediction function. The authors present an innovative unsupervised machine learning methodology for generating specific and logical test scenario catalogs leveraging K-Means++ and k-NN algorithms. The article by [PERSON] et al. (contribution 14) introduces a multidisciplinary approach aimed at understanding the impacts of rising temperatures on the retention of particulate pollutants, including heavy metals and microplastics, within road pavement materials. A soil with a particle size distribution similar to road dust was chosen for the analysis. The analysis results confirm that the viscoelastic properties of bituminous mixtures can contribute to retaining particles within the pavement under increased temperatures. The article paves the way for the development of innovative road pavement bituminous mixtures that can minimize the release of road particulate pollutants, enhancing resilience and sustainability under extreme weather conditions. The work by [PERSON] et al. (contribution 15) presents a five-degree-of-freedom nonlinear dynamic model of a vehicle with braking torque, transforming the model into an equivalent system in application of the D'Alembert principle. The equilibrium points of the equivalent system are determined by employing an improved hybrid algorithm that combines the genetic algorithm and the sequential quadratic programming method. By analyzing the bifurcation characteristics of the equilibrium points, the stability boundary at the specified initial longitudinal velocity is determined, establishing the three-dimensional stability region. The results reveal that with increasing braking torque, the number of equilibrium points increases from one to three.