A method at a sensor apparatus affixed to a transportation asset. The method includes calibrating the sensor apparatus by initiating a vertical impact at the transportation asset, measuring spring oscillation and creating a model of the transportation asset. The method further includes detecting, subsequent to the calibrating, an impact event at the sensor apparatus. The method further includes measuring spring oscillation due to the impact event at the sensor apparatus and using the measured spring oscillation in the model created during calibration to create a load mass estimate for the transportation asset.

A method for determining vehicle characteristic variables of a motor vehicle. The motor vehicle has active dampers which can set adjusting forces at the respective wheel suspensions in order to be able to raise and/or lower the body of the motor vehicle and which can also measure the acting forces. Specific predefined adjusting forces of the active dampers are imparted in order to ascertain vehicle characteristic variables from the resulting adjustment and the resulting measured forces.

A method in a first vehicle for determining a load of the first vehicle includes obtaining, from one or more sensors arranged in or at the first vehicle, an internal value of the load and an associated accuracy of the load; obtaining, from an external source, an external value and an associated accuracy of the load; and determining, based on a comparison of the respective accuracies, the load to be the internal value, the external value or a combination thereof. A method in a second vehicle for facilitating a determination of a load of a first vehicle comprises: obtaining, from one or more sensors arranged in or at the second vehicle, an internal value of the load of the first vehicle and an associated accuracy; and sharing the internal value and the associated accuracy with the first vehicle.

A vehicle includes a vehicle mass estimator circuit. The vehicle mass estimator circuit is configurable to estimate a mass of vehicle during real-time operation of the vehicle without employing a scale or dedicated mass sensing system. The vehicle mass estimator circuit generates the vehicle mass M.sub.v from: (1) an estimated torque or sensed torque, (2) an estimated motor angular velocity or sensed motor angular velocity, and (3) sensed acceleration or without any sensed acceleration. In one embodiment, the vehicle mass estimator circuit senses motor current during vehicle operation and estimates a mass of the vehicle using the motor current without any dedicated acceleration sensor such as an accelerometer. A motor angular velocity is estimated from the motor current. In another embodiment, the vehicle mass estimator circuit senses motor current during vehicle operation and estimates a mass of the vehicle using the motor current and acceleration information provided by an accelerometer.

Systems for estimating an axle load of a vehicle wherein axle load is estimated in response to an angle between two components of an axle. The angle may change as weight is added to or removed from the axle such that axle load may be determined as a function of the angle.

Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes receiving two sets of values from two sets of sensors, where a first set of sensors measure weights or pressures applied on axles of a vehicle, and where a second set of sensors measure pressures in tires of the vehicle. The method performs an error detection and removal operation to remove or filter out any erroneous values from the two sets of values to obtain two sets of filtered values. The method determines one or more values that describe a weight or pressure applied on the axle to obtain the weight distribution of the vehicle based on the first set of filtered values or the second set of filtered values. Based on the obtained weight distribution of the vehicle, the method can determine a driving operation of the vehicle.

A method at a sensor apparatus affixed to a transportation asset. The method includes calibrating the sensor apparatus by initiating a vertical impact at the transportation asset, measuring spring oscillation and creating a model of the transportation asset. The method further includes detecting, subsequent to the calibrating, an impact event at the sensor apparatus. The method further includes measuring spring oscillation due to the impact event at the sensor apparatus and using the measured spring oscillation in the model created during calibration to create a load mass estimate for the transportation asset.

A method at a computing device, the method including obtaining sensor data for a vehicle providing vibration frequency and magnitude; calculating an energy for each of a low frequency passband and a high frequency passband of a bandpass filter pair; finding an energy ratio based on the energy for the low frequency passband and the energy for the high frequency passband; applying weighting constants to each of the energy for the low frequency passband, the energy for the high frequency passband and the energy ratio to calculate a decision variable; and finding that the vehicle is unloaded if the decision variable is below a threshold and finding that the vehicle is loaded if the decision variable is above a threshold.

A method at a computing device, the method including obtaining sensor data for a vehicle providing one or both of displacement or acceleration information for the vehicle; calculating a vibration frequency for the vehicle; retrieving calibration data for the vehicle, the calibration data comprising at least a first known mass; a second known mass; a vibration frequency for the vehicle at the first known mass; and a vibration frequency of the vehicle at a second known mass; and using the calibration data and the vibration frequency for the vehicle, calculating a load estimation for the vehicle.

Methods, computer-readable media, software, and apparatuses include collecting, via one or more sensors and during a first window of time, sensor data associated with an acceleration of the vehicle, processing the sensor data to obtain frequency domain sensor data, analyzing the frequency domain sensor data to identify one or more occurrences of a vehicle mass change event, classifying a use of the vehicle for a shared mobility service during one or more time periods of the first window of time based on the one or more occurrences of a vehicle mass change, and transmitting, to a remote computing device, a notification relating to use of the vehicle for the shared mobility service.