The vehicle side slip angle represents a key indicator of dynamic stability. Elevated values of the side slip angle may indicate a loss of stability or undesired vehicle behaviors such as understeering or oversteering. With the increased use of advanced driver assistance systems (ADAS), the need for accurate estimation of the side slip angle has become increasingly critical. This quantity in general needs to be indirectly measured or estimated, with the latter often representing a more cost-effective and more reliable approach. This is usually done by simple observer design, e.g., Kalman filter, which requires a well-parameterized system dynamics model. In this work we explore Machine Learning techniques in combination with a budget hardware inertial measurement unit to estimate the sideslip angle. This is done independently of the actual vehicle configuration, i.e., vehicle load and tires used. We model the system dynamics with a traditional Luenberger Observer, Long-short-term memory, Gated recurrent unit neural networks, and their combination, and investigate possible performance benefits when incorporating well-known physical relations. The results demonstrate that a well-designed combination of model-based and data-driven approaches can achieve high estimation accuracy even without the need for large datasets, which are typically required when employing purely data-driven methods. The performance of the proposed sideslip angle estimator under different driving conditions and tire configurations is validated with real-world measurement data.

This study focuses on the use of eye-tracking technology to analyse the rider’s visual attention during racing on a Ducati Panigale V2 motorcycle. Using the TOBII Pro Glasses 2 system, the rider’s gaze dynamics were recorded, including fixations, eye movements (saccades) and gaze distribution on key sections of the track. The results revealed a link between gaze stability and cornering efficiency, particularly in optimising braking points and selecting the ideal trajectory. Identifying unstable visual behavior—such as frequent gaze deviations or constant switching between reference points—provides valuable insights for improving driving technique. This approach confirms the importance of eye-tracking as a tool for objective evaluation and optimization of rider performance in motorsport.

The aim of this article is to present universal multibody dynamic model of the heavy commercial vehicle equipped with direct yaw moment control system. The presented simulation method is based on interconnection of the multibody software ADAMS and the graphical programming environment MATLAB Simulink. The main task is to demonstrate the potential effects of the direct yaw moment control using an active differential by heavy commercial vehicle with rear wheel drive.

The goal of this article is to present a method for vehicle dynamics development based on multibody system approach in combination with real vehicle testing and data analysis. Described technique consists of two multibody models with the same level of complexity. First direct multibody model of the complete vehicle is suitable mainly for sensitivity analysis. The other inverse multibody model is used for real vehicle dynamic states reconstruction from measured data. Results of these two models are then post-processed and visualized together in a custom-made data analysis software.

This study presents a novel method for the rapid identification of key performance indicators (KPIs) from measured riding data of a Ducati Panigale V2 motorcycle, aimed at enhancing racing performance through a deeper understanding of rider-vehicle interaction. The methodology involves the design and implementation of mathematical tools within the RaceStudio3 software to analyze data from the motorcycle’s sensor system. This approach facilitates the swift detection of critical events, including gearshift delays, improper throttle control, and suspension issues. The fusion of data from the motorcycle enables a comprehensive evaluation of the rider’s influence on performance. The results demonstrate the potential of the proposed method to provide valuable insights for optimizing motorcycle setup and rider technique.