In this paper, we examine the factors affecting the behaviour of road users and the impact of these factors on professional drivers’ reliability and performance. A professional driver is considered as a driver involved in the processes of driving a vehicle as a matter of his/her business or the transportation of passengers or goods by bus or lorry, with higher liability in terms of road safety and requiring a higher degree of maintained attentiveness, constant concentration, and working capacity. This article highlights the process of selecting a professional driver by focusing on the driver’s individual and psychophysiological characteristics. An anonymous survey on professional drivers and a statistical analysis of the accidents caused by professional drivers was used to research the impact of fatigue on the behaviour of road users. The conducted statistical analysis demonstrates that the amount of professional driving experience most conducive to driving a vehicle safely was observed at between 29 and 33 years of experience. It was also found that a higher probability of causing an accident after rest days is related to the driver’s long period of engagement at work and inadequate rest. This study demonstrates that specific requirements should be applied to the methodologies for selecting professional drivers, the research on the reliability of which aims to develop a concept that enables opting for those drivers able to properly perform hired work, causing minimal losses due to road accidents.

Monitoring and control systems integrated into agricultural machinery enable the development of agricultural analyses with advanced management tools, but the full use of all available data is often limited by the lack of uniformity among data transmitted from different agricultural machines. This paper presents an agricultural data aggregation and conversion model that allows for the collection and use of data captured from different agricultural machines in the course of work; these data differ in their original file formats and cannot be combined and used in a common analysis system. Programming work was carried out to create the model, and a specialised software interface enabled raster data processing using a Python library together with the open-source Hypertext Preprocessor and JavaScript programming language libraries. A PostGIS extension was utilised to engage field geometry and map-layering tools. Model validation showed that the data aggregation and conversion functions ensure the evaluation of semantic content and the transformation of the aggregated data into a unified format which is suitable for further use in intelligent farming management applications. The developed model will encourage precision agriculture, with the aim of improving work efficiency and the rational use of resources, the economy, and ecology in agriculture.

The article describes a compression-ignition engine working with a dual-fuel system installed in diesel locomotive TEP70 BS. The model of the locomotive engine has been created applying AVL BOOST and Diesel RK software and engine performance simulations. Combustion characteristics have been identified employing the mixtures of different fuels. The paper compares ecological (CO₂, NO_x, PM) and energy (in-cylinder pressure, temperature and the rate of heat release (ROHR)) indicators of a diesel and fuel mixtures-driven locomotive. The performed simulation has shown that different fuel proportions increased methane content and decreased diesel content in the fuel mixture, as well as causing higher in-cylinder pressure and ROHR; however, in-cylinder temperature dropped. CO₂, NO_x and PM emissions decrease in all cases thus raising methane and reducing diesel content in the fuel mixture.

To determine the speed of a vehicle in a collision with body deformation, the kinetic energy input of the vehicle to cause body damage must be estimated. This paper analyzes the methods for estimating the energy equivalent of vehicle damage. A finite element model of a Toyota Yaris developed by the National Crash Analysis Center (NCAC) for use in the LS DYNA R.11.0.0 software environment is used for the simulation. The simulations include tests of the vehicle hitting a non-deformable wall, an object simulating a pole or a tree. The residual deformations obtained are used to determine the energy equivalent speed (EES) values using the “Crash 3—EBS Calculation 12.0” software and a visual comparison with the EES catalog database, where the EES parameter value is recalculated to take into account the difference in the mass of the vehicles.

Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was conducted using two publicly available datasets—Car Full View (CFV) and Freiburg Static Cars 52 v1.1 (UnsupCar)—under a fixed ConvNeXt-Small predictor with a varied training source, test target, and image preprocessing strategy. All conditions were evaluated with five-fold cross-validation at the vehicle-instance level. Annotation label incompatibility was identified as the dominant source of transfer error: correcting the angular convention mismatch in UnsupCar orientation labels reduced cross-dataset circular mean absolute error (CMAE) by approximately $3.5$–$4.5^{\circ }$. Crop protocol was a similarly large factor—train/test crop mismatch raised CMAE into the 9–${12}^{\circ }$ range. Square cropping with mirrored boundary padding provided the most robust preprocessing across both in-domain and cross-dataset conditions. After label harmonization, a residual transfer gap of approximately $2^{\circ }$ remained, with a consistent directional asymmetry favoring the UnsupCar-to-CFV transfer direction. Joint training on both harmonized datasets achieved the best-balanced performance ($3.{77}^{\circ }$ on CFV; $5.{38}^{\circ }$ on UnsupCar). These results demonstrate that instance-level splitting, explicit label harmonization, and consistent crop definition are necessary preconditions for credible cross-dataset vehicle orientation evaluation.

This study investigates the influence of suspension elastokinematics on vehicle handling and stability through a combined research of experimental testing and numerical simulation. Laboratory tests were conducted on the front suspension of a Mercedes-Benz S320 using a quarter-car test rig equipped with specialized sensors to measure wheel displacements, steering angles, camber, and accelerations. Complementary dynamic tests were carried out under real driving conditions, including braking in a turn and “fishhook” maneuvers, to capture suspension behavior under critical operating scenarios. Based on the experimental data, an MSC Adams/Car multibody simulation model was used, incorporating varying stiffness values of suspension elastomeric elements that replicated progressive aging and degradation effects. The simulation results were compared with experimental data to validate the model’s predictive capability. Key findings indicate that reductions in elastomer stiffness significantly affect wheel kinematics, vehicle yaw response, and lateral acceleration, particularly during high-intensity maneuvers. The results underline the critical importance of accounting for elastomeric component degradation in suspension modeling to ensure vehicle safety and performance over the operational lifespan. The developed methodology demonstrates the effectiveness of integrating experimental measurements with advanced simulation tools to assess elastokinematic effects on vehicle dynamics.