Estimation of Vehicle Longitudinal Velocity with Artificial Neural Network

Vehicle dynamics control systems have a fundamental role in smart and autonomous mobility, where one of the most crucial aspects is the vehicle body velocity estimation. In this paper, the problem of a correct evaluation of the vehicle longitudinal velocity for dynamic control applications is approached using a neural networks technique employing a set of measured samples referring to signals usually available on-board, such as longitudinal and lateral acceleration, steering angle, yaw rate and linear wheel speed. Experiments were run on four professional driving circuits with very different characteristics, and the vehicle longitudinal velocity was estimated with different neural network training policies and validated through comparison with the measurements of the one acquired at the vehicle’s center of gravity, provided by an optical Correvit sensor, which serves as the reference (and, therefore, exact) velocity values. The results obtained with the proposed methodology are in good agreement with the reference values in almost all tested conditions, covering both the linear and the nonlinear behavior of the car, proving that artificial neural networks can be efficiently employed onboard, thereby enriching the standard set of control and safety-related electronics

Sport driving skills: A preliminary comparative study from outdoor testing sessions

The optimization of vehicle handling is a multifaceted process that extends beyond the vehicle’s design and engineering. This work focuses on the fundamental role that drivers play in shaping the vehicle’s overall behavior. While technological advancements have significantly impacted the automotive industry, defining new methodologies and approaches for vehicle controls, there is not yet a uniquely recognized procedure to objectively define the skills and weaknesses of pilots. This paper aims to present the preliminary results of an innovative study, based on an outdoor test campaign with a fully instrumented vehicle, driven on track by several drivers with different levels of experience. Starting from the collected data, a series of objective and generalized metrics have been defined in order to quantify different aspects related to the direct driver interaction with the car and to the trajectory repeatability. By analyzing the results obtained from these metrics, it has been possible to highlight the differences among the participants in the experimental campaign. In order to create a practical visualization of the goodness of the approach, a driver ranking has been defined and it is coherent with both the best lap times obtained by the drivers and their actual experience