As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

One of the technologies used for localization is GNSS (Global Navigation Satellite Systems), which is exposed to many phenomena, i.e., the occurrence of terrain covers, reflections of the radio waves, and the multipath nature of the radio waves in the propagation environment. To increase the resistance to environmental phenomena, special types of antennas are used, which include, among others, choke ring antennas. The article describes the design and construction of the choke ring antenna and the impact of the mentioned device on the quality of GNSS positioning. The accuracy of the constructed antenna (based on selected accuracy measures: DRMS, 2DRMS, SEP, MRSE, SAE) is calculated together with positioning errors determined for two types of antennas: choke ring antenna and the dedicated antenna supplied by the manufacturer in RTK mode. The results confirm that the designed choke ring antenna can be used for positioning without significantly losing accuracy in the RTK mode.

The article describes the issues of transport of bulk materials. The knowledge of this process has a key impact on the rational planning of transport tasks. It is necessary to have knowledge about the transport services market and the competition that exists in it. In order to achieve a competitive advantage on the market, enterprises should analyze data on the implementation of transport tasks on an ongoing basis. It is also important that the costs incurred from the conducted activity are minimized, while increasing the quality of services and taking into account the sustainable development of the enterprise. The study analyzes data from a few selected motor vehicles in the period of 3 years of operation, coming from an enterprise specializing in the transport of bulk materials. Moreover, a global sensitivity analysis was performed based on a neural model describing the impact of the analyzed factors on the company’s profit. The results show that the most important factors influencing the company’s profit are the fuel consumption of individual vehicles, the driver (driving style) and the month (average temperature, weather conditions).

One of the main threats to ecological safety is the increased emissions of greenhouse gases. Promoting the purchase of electric vehicles and increasing their share among all cars in a given country can be considered as activities reducing the emissions of CO₂ into the atmosphere. Based on Environmental Performance Index, in 2021, Poland is in 37th place among the most climate-friendly countries in the world, and 30th among similar countries in Europe. The aim of the article was to model the prices of electric vehicles as one of the elements of promoting climate security in Poland. For the purposes of the study, an analysis of data from electric vehicle sales advertisements on one of the Polish automotive services was carried out. Moreover, on this basis, the most important factors influencing the price of the vehicle were analyzed. For this purpose, forecasting models were built based on neural networks and selected models of decision trees based on the CART algorithm, boosted trees, and random forest. We assessed the developed models and compared their prognostic abilities.

The article analyzes the impact of selected operational parameters of internal combustion engine vehicles on CO₂ emissions. The study was preceded by a detailed analysis of the issues related to CO₂ emissions in the EU, with a focus on Poland, where the tests were conducted. The key scientific assumption is that individual vehicle users’ behaviors significantly impact global CO₂ emissions. Daily use of private vehicles, driving style, and attention to fuel efficiency contribute to cumulative effects that can drive the transformation toward more sustainable transport. Therefore, the study was conducted using real-time empirical data obtained from the vehicles’ OBD (On-Board Diagnostics) diagnostic systems. This approach enabled the creation of a diagnostic tool allowing each vehicle user to assess CO₂ emissions and ultimately manage its levels, which is the biggest innovation of the work. Two levels of CO₂ emissions were identified as categorical variables in the model, considered either ecological or non-ecological from the perspective of sustainable transport. The CO₂ emission threshold of 200 g/km was adopted based on the average age of vehicles in Poland (14.5 years) and Regulation (EC) No 443/2009 of the European Parliament and of the Council. Three models of logistic regression dedicated to different driving cycle phases—starting, urban driving, and highway driving—were proposed and compared. This study demonstrated that during vehicle starting, the most significant factors influencing the probability of ecological driving are vehicle velocity, relative engine load, and relative throttle position, while for the other two types of movement, engine power and torque should also be considered. The logistic regression model for vehicle start-up obtained a value of sensitivity at about 82% and precision at about 85%. In the case of urban driving, the values of the discussed parameters reach significantly higher levels, with sensitivity at around 96% and precision at about 92%. In turn, the model related to highway driving achieved the highest values among the created models, with sensitivity at around 97% and precision at about 93%.

Unmanned ground vehicles (UGVs) are technically complex machines to operate in difficult or dangerous environmental conditions. In recent years, there has been an increase in research on so called “following vehicles”. The said concept introduces a guide—an object that sets the route the platform should follow. Afterwards, the role of the UGV is to reproduce the mentioned path. The article is based on the field test results of an outdoor localization subsystem using ultra-wideband technology. It focuses on determining the guide’s route using a smoothing spline for constructing a UGV’s path planning subsystem, which is one of the stages for implementing a “follow-me” system. It has been shown that the use of a smoothing spline, due to the implemented mathematical model, allows for recreating the guide’s path in the event of data decay lasting up to a several seconds. The innovation of this article originates from influencing studies on the smoothing parameter of the estimation errors of the guide’s location.