Abstract: Disclosed are examples for detecting a displacement of a sensor within a tire. A baseline signal strength is compared to a measured signal strength. When a calculated comparison value (e.g., delta standard deviation) meets or exceeds a threshold value, the sensor is determined to be displaced from the expected location in the tire. When a sensor is determined to be displaced or otherwise loose from the expected location within the tire, an alert can be generated to provide notice to the vehicle driver or other entity of the issue so that the issue can then be remedied.

Abstract: Disclosed are various approaches for estimating tread wear condition for a tire. Segment analysis data for a given segment and a given tire can be obtained. The segment analysis data can include tire data, vehicle data, or manual inspection data. A starting tread depth corresponding to a tread depth of a tire at a segment start can be determined. A segment distance corresponding to a distance the tire has traveled up to a segment end is determined. The starting tread depth and the segment distance are applied as inputs to a trained tread wear condition model. A tread wear condition is predicted based at least in part on an output of the tread wear condition model.

Abstract: Disclosed are examples for detecting a displacement of a sensor within a tire. A baseline signal strength is compared to a measured signal strength. When a calculated comparison value (e.g., delta standard deviation) meets or exceeds a threshold value, the sensor is determined to be displaced from the expected location in the tire. When a sensor is determined to be displaced or otherwise loose from the expected location within the tire, an alert can be generated to provide notice to the vehicle driver or other entity of the issue so that the issue can then be remedied.