Knowledge of the tire-road friction condition is an important key for driver safety and vehicle stability systems during ice and snow road. In particular, friction information can be used to enhance the performance of Advanced Driver-Assistance Systems (ADAS) applications providing real-time forces condition. The paper deals with a method for real-time identification of tyre/road friction condition using both Pacejka model and steady state form of distributed LuGre to obtain the friction based on recursive nonlinear optimization of the curve fitting errors. Finally, the effectiveness of the method is confirmed by real-time simulations in ice and snow conditions.
Tyre Models for Online Identification in ADAS Applications
Sharifzadeh M.
Investigation
;Senatore A.Writing – Review & Editing
2019-01-01
Abstract
Knowledge of the tire-road friction condition is an important key for driver safety and vehicle stability systems during ice and snow road. In particular, friction information can be used to enhance the performance of Advanced Driver-Assistance Systems (ADAS) applications providing real-time forces condition. The paper deals with a method for real-time identification of tyre/road friction condition using both Pacejka model and steady state form of distributed LuGre to obtain the friction based on recursive nonlinear optimization of the curve fitting errors. Finally, the effectiveness of the method is confirmed by real-time simulations in ice and snow conditions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
