This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision.

Vehicle stability during cornering on horizontal road curves is a risky stage of travel because of additional factors acting. The main stability factor is centrifugal force, which depends on road curve sharpness and is very sensitive to driving speed usually controlled by the driver. However, the counterforce is produced at tire-road interaction, where different pavement types and states cause a wide variation of tire contact forces and vehicle stability. In the paper, the part of vehicle suspension performance while moving on a sharp horizontal road curve with different levels of pavement roughness was simulated by 14 degrees of freedom vehicle model. The model was built in MATLAB/Simulink software with available pavement roughness selection according to ISO 8608. The influence of variable suspension damping available in modern vehicles on risky cornering is analysed when a vehicle reaches the edge of the pavement with its specific roughness. Critical parameters of vehicle stability depending on road curvature, pavement roughness and driving speed are selected to assess the solutions for safe cornering.

This research focuses on vehicle Advanced Driver Assistance Systems (ADAS), with particular emphasis on Lane Keeping Assist (LKA) systems which is designed to help drivers keep a vehicle centered within its lane and reduce the risk of unintentional lane departures. These kinds of systems detect lane boundaries using computer vision algorithms applied to video data captured by a forward-facing camera and interpret this visual information to provide corrective steering inputs or driver alerts. The research investigates the performance, reliability, sustainability, and limitations of LKA systems under adverse road and environmental conditions, such as wet pavement and in the presence of degraded, partially visible, or missing horizontal road markings. Improving the reliability of lane detection and keeping systems enhances road safety, reducing traffic accidents caused by lane departures, which directly supports social sustainability. For the theoretical test, a modified road model using MATLAB software was used to simulate poor road markings and to investigate possible test outcomes. A series of field tests were conducted on multiple passenger vehicles equipped with LKA technologies to evaluate their response in real-world scenarios. The results show that it is very important to ensure high quality horizontal road markings as specified in UNECE Regulation No. 130, as lane keeping aids are not uniformly effective. Furthermore, the study highlights the need to develop more robust line detection algorithms capable of adapting to diverse road and weather conditions, thereby enhancing overall driving safety and system reliability. LKA system research supports sustainable mobility strategies promoted by international organizations—aiming to transition to safer, smarter, and less polluting transportation systems.

The present study explores the effect of incorrect tire mounting on vehicle braking performance with a specific focus on its interaction with the Anti-Lock Braking System (ABS). Experimental testing was conducted utilizing a passenger vehicle under controlled conditions according to ISO 21994. Two tire configurations were studied: a complying axle-wise configuration and a non-compliant asymmetric arrangement with the same tires mounted longitudinally on each side of the vehicle. ABS was switched on and off throughout the braking testing. The dynamics of the vehicle and its brake parameters were recorded and analyzed using nonlinear statistical modelling. The obtained coefficients of determination of the suggested nonlinear model were in the range of 0.8265 to 0.9938 and showed a satisfactory agreement with the experimental data. Chow-type statistical testing revealed significant differences across all tire designs and ABS operational modes tested (p < 0.001). Ljung–Box test indicated the lateral acceleration signals were significantly temporally dependent. The results indicate that the incorrect asymmetric tire mounting has a negative influence on the braking dynamics and this effect is more significant when the ABS is turned off. While ABS mitigates these effects to some degree, we still observe statistically significant disparities in performance. The results give empirically validated proof of the need for proper tire installation for the preservation of braking performance and vehicle safety and contribute to a better understanding of tire–road interaction and braking dynamics under asymmetric tire configurations.