Abstract: Systems and methods are disclosed herein for tire condition monitoring, and more particularly for detecting slow leakage of inflation pressure. Data acquisition devices (e.g., tire pressure monitoring system sensors) are mounted onboard motor vehicles and collect data samples corresponding to at least tire inflation pressure. The collected data samples may e.g. only be transmitted for analysis while the motor vehicle is in a fleet yard or otherwise wherein the contained air temperature effectively matches an ambient temperature. A time elapsed is calculated from a first data sample within a defined sampling period, and a statistical model is applied for at least the data samples corresponding to inflation pressure with respect to the time elapsed. A tire slow leak event is ascertained based on an evaluated amount of decrease in the inflation pressure from the statistical model, and an output signal is selectively generated corresponding to the ascertained slow leak event.

Abstract: A computer-implemented method enables vehicle tire wear prediction based on minimal input data points. Information is aggregated in a hierarchical data structure regarding historical tread values for multiple tires, and respective values associated with the historical tread values for each of multiple parameters that are hierarchically defined from highest to lowest levels. A current tire tread value is provided from a sensor associated with a first tire, and respective values associated with the current tire tread value are provided for each of the multiple hierarchically defined parameters. The current tire tread value is matched with information from the hierarchical data structure corresponding to matching values for one or more of the hierarchically defined parameters having at least a predetermined threshold number of available tread values, and a tire wear rate is predicted for the first tire based at least in part on the matched information from the hierarchical data structure.

Abstract: A computer-implemented method enables vehicle tire wear prediction based on minimal input data points. Information is aggregated in a hierarchical data structure regarding historical tread values for multiple tires, and respective values associated with the historical tread values for each of multiple parameters that are hierarchically defined from highest to lowest levels. A current tire tread value is provided from a sensor associated with a first tire, and respective values associated with the current tire tread value are provided for each of the multiple hierarchically defined parameters. The current tire tread value is matched with information from the hierarchical data structure corresponding to matching values for one or more of the hierarchically defined parameters having at least a predetermined threshold number of available tread values, and a tire wear rate is predicted for the first tire based at least in part on the matched information from the hierarchical data structure.