Braking systems development is currently facing some huge challenges. The first one is electrification. The presence of regenerative braking and the standard adoption of electro-hydraulic braking systems is completely changing the way braking systems are developed and validated. The second one is connectivity. This concept is not only applying to the vehicle itself, but also to the development process. Currently vehicles are tested in multiple locations on a worldwide basis. Witnessing testing is becoming every day more expensive and complicated, particularly for vehicles which are sold in different markets. The third big challenge is certainly the increasing level of driving automation. The pure concept of vehicle assessment and sign-off is shifted from the driver perspective to the “occupant” perspective. Comfort cannot be jeopardized, a good balance between performance and refinement must be guaranteed. To respond to these three big challenges to brake development, the authors are presenting how artificial intelligence can support the identification of brake noise in real time. In this paper a summary of the machine learning techniques used to identify brake noise events are presented. Particularly, the validation of the algorithm is presented in order to cover not only brake squeal in standard condition, but also to detect different brake noises, under different testing conditions (different standards, city and mountain driving, low and high ambient temperature, different vehicle category). Finally, the authors are presenting an automatic process which is managing the complete process, from driving and rating, through detection, brake noise automated analysis and finally the upload of the testing report and relevant information in a connected secure environment.

Applus IDIADA: Mr. Antonio Rubio, Ing. Fabio Squadrani, Mr. Danilo Mendes Pedroso, Dr. Eng. Juan J. García Bonito, Ing. Angelo Vitale, Mr. Juan Pablo Barles, Mr. Antonio Jesús Contreras, Mr. Jose Francisco Martinez, Mr.Kenneth Mendoza