Method for determining a tread depth of a vehicle tyre, comprising the following steps: a) providing a vehicle tyre having a known tread depth, b) determining a predicted tread depth and an uncertainty of the prediction using a first method, tread depth being ascertained by way of an evaluation of at least one boundary condition that influences tyre wear on the vehicle, c) ascertaining a predicted reference tread depth of the vehicle tyre by way of a measurement using a second method, the uncertainty of the measurement likewise being known, d) providing a signal processing device, the tread depth predicted using the first method and the predicted reference tread depth being gathered in the signal processing device and processed using an information fusion unit, the information fusion unit comprising a Kalman filter, e) processing the tread depth predicted using the first method with the associated uncertainty of the prediction and the reference tread depth predicted using the second method with the associated uncertainty of the measurement using the Kalman filter, a prediction step and a correction step being carried out iteratively and then a fused result being provided for the tread depth, wherein the first method involves the boundary conditions comprising a multiplicity of driving parameters, the driving parameters involving at least the speeds and the steering angles of the vehicle being recorded and evaluated. a) providing a vehicle tyre having a known tread depth, b) determining a predicted tread depth and an uncertainty of the prediction using a first method, tread depth being ascertained by way of an evaluation of at least one boundary condition that influences tyre wear on the vehicle, c) ascertaining a predicted reference tread depth of the vehicle tyre by way of a measurement using a second method, the uncertainty of the measurement likewise being known, d) providing a signal processing device, the tread depth predicted using the first method and the predicted reference tread depth being gathered in the signal processing device and processed using an information fusion unit, the information fusion unit comprising a Kalman filter, the tread depth predicted using the first method and the predicted reference tread depth being gathered in the signal processing device and processed using an information fusion unit, the information fusion unit comprising a Kalman filter, e) processing the tread depth predicted using the first method with the associated uncertainty of the prediction and the reference tread depth predicted using the second method with the associated uncertainty of the measurement using the Kalman filter, a prediction step and a correction step being carried out iteratively and then a fused result being provided for the tread depth, wherein the first method involves the boundary conditions comprising a multiplicity of driving parameters, the driving parameters involving at least the speeds and the steering angles of the vehicle being recorded and evaluated. a prediction step and a correction step being carried out iteratively and then a fused result being provided for the tread depth, wherein the first method involves the boundary conditions comprising a multiplicity of driving parameters, the driving parameters involving at least the speeds and the steering angles of the vehicle being recorded and evaluated.