Longitudinal Force Estimation in Intelligent Tires Using Key Features and Tread Dynamics Validation

Intelligent tire systems have garnered considerable interest as a technology that enhances tire safety through the monitoring of tire characteristics and tire–road interactions, which directly influence vehicle dynamics. However, battery life limitations owing to computational demands constrain their practical implementation. This study focused on studless winter tires to improve safety on icy and snowy roads, where freezing and snow accumulation increase braking distances, elevating the risk of accidents. Specifically, we developed computationally efficient features from tire acceleration signals to estimate longitudinal force, a key factor in tire–road interaction. Acceleration signals were analyzed to extract features most effective for force estimation. To reduce power consumption, only the most relevant features were selected. The selected features were applied to a machine learning model (ExtraTree regressor) to estimate longitudinal force. The method achieved high estimation accuracy with a normalized root mean square error (NRMSE) of 3.3%, while significantly minimizing computational load and power consumption. Compared to transmitting raw signals, the proposed approach reduced power consumption from 49.4 mW to 0.11 mW per second. Direct observations of the tire–road contact patch using a high-speed camera were conducted to validate the features. Time–frequency analysis of acceleration signals further supported the features' effectiveness, revealing that they correspond to tread vibrations caused by the relaxation phenomenon, where deformed tread elements recover after road contact. The proposed approach offers a promising method to enhance safety and efficiency in winter driving conditions by providing accurate, real-time tire–road interaction data while conserving energy.