Intelligent sensing and control for Automotive Human Machine Interface



September 1, 2010 – August 31, 2015



S. Ferrari


G. Sapiro (Duke University), A. Visconti (Ferrari SpA)

Project Description

This project develops new methods inspired by modern control theory, computational intelligence, and data mining, for the design of human-machine interfaces that adapt the dynamic response of the automobile to the driver, in order to enhance the driver’s experience and the automobile’s performance in the closed loop.

Research Goals

  • Integration of remote sensing and information processing for driver state inference
  • Application of adaptive feedback control for driver experience improvement
  • Development of displays and ambiance design

Peer-Reviewed Publications

  1. H. Wei, W. Ross, S. Varisco, P. Krief, and S. Ferrari, “Modeling of Human Driver Behavior via Receding Horizon and Artificial Neural Network Controllers,” Proc. of the IEEE Conference on Decision and Control, Florence, Italy, December 2013. [PDF][Slides]


  1. “Human Driver Modeling via Arti cial Neural Networks,” Veicolo Group, Ferrari S.p.A., Maranello (MO), Italy, January 2013.
  2. “Modeling Human Pilots with Artificial Neural Networks,” IEEE Conference on Decision and Control, Florence, Italy, December 2013. [Slides]