Abstract: A system for determining a tire pressure status in a vehicle is provided. The system includes at least one processing device, which may be arranged to: estimate values of a set of predetermined vehicle parameters; calculate a tire pressure parameter as a predetermined function of the values of the set of predetermined vehicle parameters, wherein said predetermined function has been generated by training a machine learning system, such as, for example, a neural network or a Support Vector Machine, using values of a large number of sets of predetermined vehicle parameters and associated tire pressure parameters; and determine a tire pressure status, indicating whether or not there is tire under-inflation, by comparing said tire pressure parameter with a threshold.

Abstract: A system for determining a tire change status in a vehicle is provided. The system includes at least one processing device and a storage device. The at least one processing device may be arranged to: calculate a value of at least one reference parameter which may be used in indirect tire pressure monitoring; store the calculated value of said at least one reference parameter in the storage device; retrieve a previously stored value of said at least one reference parameter from the storage device; determine a similarity parameter based on a similarity between the calculated value and the previously stored value of said at least one reference parameter; and determine a tire change status, indicating whether or not the tires have been re-filled or replaced, by comparing said similarity parameter with a threshold.

Abstract: The present disclosure relates to the estimation of a velocity of a first object using accelerometer signals, indicating an acceleration of the first object and/or a second object coupled to a first object. To this end, a characteristic frequency in the accelerometer signal spectrum may be determined, preferably by applying a parametric model or by performing a spectrum analysis, and used as a basis to estimate the velocity of the first object based on the determined characteristic frequency. The characteristic frequency may be determined by identifying the frequency having the maximum spectral amplitude or by identifying the fundamental frequency or a particular harmonic in the spectrum.

Abstract: A system for determining a tire pressure status in a vehicle is provided. The system includes at least one processing device, which may be arranged to: estimate values of a set of predetermined vehicle parameters; calculate a tire pressure parameter as a predetermined function of the values of the set of predetermined vehicle parameters, wherein said predetermined function has been generated by training a machine learning system, such as, for example, a neural network or a Support Vector Machine, using values of a large number of sets of predetermined vehicle parameters and associated tire pressure parameters; and determine a tire pressure status, indicating whether or not there is tire under-inflation, by comparing said tire pressure parameter with a threshold.

Abstract: A system for determining a tire change status in a vehicle is provided. The system includes at least one processing device and a storage device. The at least one processing device may be arranged to: calculate a value of at least one reference parameter which may be used in indirect tire pressure monitoring; store the calculated value of said at least one reference parameter in the storage device; retrieve a previously stored value of said at least one reference parameter from the storage device; determine a similarity parameter based on a similarity between the calculated value and the previously stored value of said at least one reference parameter; and determine a tire change status, indicating whether or not the tires have been re-filled or replaced, by comparing said similarity parameter with a threshold.

Abstract: The present disclosure relates to the estimation of a velocity of a first object using accelerometer signals, indicating an acceleration of the first object and/or a second object coupled to a first object. To this end, a characteristic frequency in the accelerometer signal spectrum may be determined, preferably by applying a parametric model or by performing a spectrum analysis, and used as a basis to estimate the velocity of the first object based on the determined characteristic frequency. The characteristic frequency may be determined by identifying the frequency having the maximum spectral amplitude or by identifying the fundamental frequency or a particular harmonic in the spectrum.

Abstract: A system and a method of determining a tire class mounted on a wheel of a driving vehicle based on at least one sensor signal received from a sensors comprised in the vehicle.

Abstract: A method, an apparatus and a computer program product for estimating the current load of a vehicle wherein a filter bank including a filter for different weight classes, each filter implementing a vehicle model for estimating the current mass of the vehicle. Based on vehicle data indicative of a current driving situation of the vehicle, and filter parameter specific for the respective weight class, each filter provides a load estimation value as filter specific estimation of the current load of the vehicle.

Abstract: A method, an apparatus and a computer program product for estimating the current load of a vehicle wherein a filter bank including a filter for different weight classes, each filter implementing a vehicle model for estimating the current mass of the vehicle. Based on vehicle data indicative of a current driving situation of the vehicle, and filter parameter specific for the respective weight class, each filter provides a load estimation value as filter specific estimation of the current load of the vehicle.