A system and method are provided for estimating and applying vehicle tire traction. Vehicle data (e.g., movement and location-based data) and tire sensor data are collected at a vehicle and transmitted to a remote computing system (e.g., cloud server). A wear status is determined, and traction characteristics determined for at least one tire, based at least on the vehicle data and the determined tire wear status. The predicted tire traction characteristics are transmitted from the remote computing system to an active safety unit associated with the vehicle, or a fleet management system, wherein the recipient is configured to modify vehicle operation settings based on at least the predicted tire traction characteristics. A maximum speed for the vehicle may be defined by the recipient, or a minimum following distance where, e.g., the vehicle is one of multiple vehicles in a defined platoon.

A tire pressure monitoring unit is mounted on a vehicle wheel having a pressure and acceleration sensor. A method for assigning wheel positions includes pressure and acceleration data determined from the sensors. The pressure data and at least one piece of information derived from the acceleration data are transmitted wirelessly in a data telegram together with a characteristic identifier. Information concerning the reliability of the information derived from the acceleration data is acquired in the tire pressure monitoring unit on the basis of the measurements and is transmitted with the data telegram. A central evaluation unit considers a plurality of data telegrams and, taking account of data from the ABS sensors, assigns the individual tire pressure monitoring units to wheel positions. Data telegrams in which the reliability is higher receive a greater weighting, and data telegrams in which the reliability is smaller receive a smaller weighting.

In a braking performance evaluation method including the steps of acquiring a tire ground contact pressure distribution, acquiring a sliding friction coefficient table, and calculating a friction force of an entire tire using a brush model having a function representing the tire ground contact pressure distribution and the sliding friction coefficient table, the step of acquiring the tire ground contact pressure distribution includes the step of acquiring a first ground contact pressure distribution on a road surface on which no water film is present via actual measurement or calculation and the step of acquiring a second ground contact pressure distribution by applying reduction in a ground contact pressure due to a water film intruded between the tire and the road surface to the first ground contact pressure distribution and using the second ground contact pressure distribution as the tire ground contact pressure distribution used for the calculating.

Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes determining a vehicle weight distribution that values for each axle of the vehicle that describe weight or pressure applied on a respective axle. The values of the vehicle weight distribution are determined by removing at least one value that is outside a range of pre-determined values from a set of sensor values. The method further includes determining a driving-related operation of the vehicle weight distribution. For example, the driving-related operation may include determining a braking amount for each axle and/or determining a maximum steering angle to operate the vehicle. The method further includes controlling one or more subsystems in the vehicle via an instruction related to the driving-related operation. For example, transmitting the instruction to the one or more subsystems causes the vehicle to perform the driving-related operation.

Embodiments may provide a system, a wheel localizer, a wheel localization device, or methods for locating a position of at least one wheel out of a plurality of wheels of a vehicle. In one embodiment, a system comprises a detector that obtains information related to a reference magnetic field in which the at least one wheel rotates, an antilock braking system (ABS) unit that obtains information related to angular rotations of the plurality of wheels, and a locator that determines the position of the at least one wheel based, at least in part, on the information related to the reference magnetic field and the information related to the angular rotations of plurality of wheels, where the position comprises a wheel location from among the plurality of wheels. The reference magnetic field may be the earth's magnetic field.

A method for adjusting one or more vehicle dynamics systems of a vehicle, the vehicle comprising a road wheel and at least one vehicle sensor configured to provide vehicle condition data, the road wheel comprising a tyre sensor configured to output tyre operation data, the method comprising: receiving tyre operation data from the tyre sensor; receiving vehicle condition data from at least one vehicle sensor; calculating one or more vehicle dynamics parameters based on the vehicle condition data and the tyre operation data; and adjusting one or more vehicle dynamics systems in response to the calculated one or more vehicle dynamics parameters.

A motion control system for controlling one or more torque generating devices on a heavy-duty vehicle, the system comprising a primary sensor system with a primary sensor control unit configured to interpret an output signal of the primary sensor system, one or more tire sensor devices mounted on, in, or in connection to, one or more tires of the heavy-duty vehicle, and a tire sensor control unit configured to interpret an output signal of the one or more tire sensor devices, wherein the motion control system is arranged to base motion control of the heavy-duty vehicle on output data of the tire sensor control unit in case of malfunction in the primary sensor system and/or in the primary sensor control unit, and on output data of the primary sensor control unit otherwise.

A wheel end computer, WEC, (220) for hosting and executing one or more motion support device abstraction modules (MSDA, 221) configured to monitor and/or to control operations of one or more respective motion support devices, MSDs, (240, 250, 260, 270) on a heavy duty vehicle, where an MSDA provides a control and/or a monitoring interface between an external vehicle unit computer (VUC, 210), and a respective MSDs operational functionality, wherein the WEC (220) is arranged to identify a matching MSDA for each MSD in a set of MSDs, such that each MSD connected to the WEC is matched to a respective MSDA, and wherein the WEC (220) is arranged to receive a monitor and/or a control command from the VUC (210) for monitoring and/or controlling an MSD connected to the WEC, and to control the MSD via the respective matching MSDA.

A system and method are provided for estimating and applying vehicle tire traction. Vehicle data (e.g., movement and location-based data) and tire sensor data are collected at a vehicle and transmitted to a remote computing system (e.g., cloud server). A wear status is determined, and traction characteristics determined for at least one tire, based at least on the vehicle data and the determined tire wear status. The predicted tire traction characteristics are transmitted from the remote computing system to an active safety unit associated with the vehicle, or a fleet management system, wherein the recipient is configured to modify vehicle operation settings based on at least the predicted tire traction characteristics. A maximum speed for the vehicle may be defined by the recipient, or a minimum following distance where, e.g., the vehicle is one of multiple vehicles in a defined platoon.

A tire mount sensor detects a road surface condition such as a type of a road surface and a road surface , and transmits road surface data indicating a detection result to a communication center. The communication center collects road surface data more precisely, and the vehicle receives the more precise road surface data from the communication center. Based on received more precise road surface data, the risk of the vehicle is determined. Thus, the road surface condition is detected using the tire mount sensor, so that the road surface condition is detected without braking. Accordingly, it is possible to detect the road surface condition with high frequency, so that the road surface condition is detected in wider area, and it is possible to perform the control more appropriately for avoiding the risk based on the road surface condition during a travel.