Abstract
The design of most subsystems of a vehicle are becoming more and more critical, because of the timing for the modern projects. The subsystem related to the active chassis controls belongs to that basket and their design and refinement is becoming even more critical with the new non-traditional powertrain architectures. In such applications, indeed, the integration of all the vehicle subsystems increases its complexity.
The presentation shows a new method to merge together the typical offline simulation with track data measurements and prevision of the expected performance of new components design done by a DiM150 dynamic driving simulator (please refer to https://www.youtube.com/watch?v=pmJuepiHPzA).
In particular, the presentation refers to ADAS control systems virtually tuned to shorten the physical testing on road and track (or, at least, reduce the number of physical prototypes to be created and then tested on the vehicle).
Machine learning related applications are becoming common in the automotive industry especially for what concerns ADAS and AD functionalities. The use of simulations as well as CGI can be exploited at first as a more affordable and customizable data source. Moreover the use of driving simulators can assure the introduction of human factors into the development loop in earlier design phases.
About the speakers
Claudio Ricci received the Master degree in Mechanical Engineering from the University of Genoa. In 1996 he joined FIAT R&D Center researchiing vehicle dynamics, cooperating with F1 Team for ten years. He left as Driveability Unit Manager in 2014 when he became Team Leader of the Performance Engineers for "small cars" range (Panda, 500X, Abarth 695). From 2016 till November 2018 he was in charge of sport activities of 124 Abarth. He joined Danisi Engineering in December 2018 as Head of Advanced Vehicle Dynamic Center.
Stefano Ballesio received the Master degree in Aerospace Engineering from the Politecnico of Turin with a major in Flight Mechanics and Aerospace Systems. In 2015 he joined Add.fo team as Data Scientist and Analyst, getting involved in the development of virtual sensors for automotive applications, data analysis for fleet management and autosport, development of advanced control based on machine learning algorithms and reinforcement learning.