A method of setting the rides height of the air springs and air pressures of the tires, including receiving a user selected setting or preprogrammed ride height settings; sensing a ride height of, and air pressure within, each of the air springs; determining the weight of the vehicle based on the sensed ride height and air pressure within each of the air springs; providing specified ride heights for the left and right front and rear air springs; determining specified air pressures for the left and right front and rear tire inflators, based upon the determined weight of the vehicle and selected setting; inflating the left and right front and rear air springs to the specified ride heights; and inflating the left and right front and rear tires to the specified air pressures.

A method of controlling driving force of a vehicle includes estimating a first maximum road surface frictional coefficient based on a driving stiffness defined by a micro slip ratio and driving force of drive wheels, in a first driving state where the vehicle travels straight at a constant acceleration, estimating a second maximum road surface frictional coefficient based on a steering reaction force detected by an electric power steering device, in a second driving state different from the first state and where the vehicle is steered, estimating a third maximum road surface frictional coefficient to be a given value in a third driving state different from the first and second states and where an outdoor air temperature is above a determination temperature, and controlling the driving force to settle within a friction circle defined by each of the highest frictional coefficients and a ground contact load of the drive wheels.

An attitude estimation apparatus includes an attitude estimation unit for estimating the roll angle and pitch angle of a movable body and for using a calculation process to estimate an offset error for at least one of first, second and third angular velocity detection units and first, second and third acceleration detection units. The attitude estimation unit estimates the roll angle and pitch angle of the movable body and the offset error for at least one detection unit based on detected values from the first, second and third angular velocity detection units, detected values from the first, second and third acceleration detection units, a detected value from the velocity information detection unit, an estimated value of the roll angle from a previous estimation operation, an estimated value of the pitch angle from the previous estimation operation, and an estimated value of the offset error from the previous estimation operation.

Longitudinal acceleration, intended travel angle, wheel speed, and requested drive torque signals are measured for a vehicle. The longitudinal acceleration, intended travel angle, wheel speed, and requested drive torque signals are then evaluated. A brake torque is calculated as a function of a propulsive torque, wherein the propulsive torque is produced by a power source for the vehicle. The brake torque is applied when the longitudinal acceleration signal exceeds a longitudinal acceleration threshold, the intended travel angle signal is between intended travel angle limits, the wheel speed signal is less than a minimum speed threshold, the requested drive torque signal exceeds a requested drive torque threshold, and a torque threshold is exceeded.

A yaw motion control method for a four-wheel distributed vehicle includes: calculating the steering response of the vehicle in a steady state using a nonlinear vehicle model in reference with an understeering degree while constraining by the limit value of the road surface adhesion condition according to the sideslip angle response and the vertical load change in the steady state, calculating the lateral force response and the self-aligning moment response of the tires in the steady state by a magic tire formula, calculating the required additional yaw moment by using the yaw motion balance equation, reasonably distributing the generalized control force to the four drive motors through the optimization algorithm in combination with the current driving conditions; finally, off-line storing and retrieving the calculation results of the off-line distribution of different vehicle parameters required by different upper layers to distribute the torques to the four drive wheels.

A wheel load estimation method of a four-wheel drive vehicle driven by a rotational driving device comprises a correlation relationship setting step for previously setting a correlation relationship between a total weight and at least one of the front wheel load and the rear wheel load by variously changing a movable load of the vehicle, a total vehicle weight computation step for calculating a current total vehicle weight from an output torque of the rotational driving device and a longitudinal acceleration of the vehicle corresponding to the output torque, and a wheel load estimation step for estimating the wheel load of at least a driving wheel from the correlation relationship and the total vehicle weight.

A system for estimating a tire load of a tire includes a pressure sensor configured to generate a tire pressure signal; an acceleration sensor configured to generate a tire acceleration signal; a temperature sensor configured to generate a tire temperature signal; and at least one processor configured to calculate a duration of a contact patch based on the tire acceleration signal, calculate a vehicle speed based on the tire acceleration signal, determine at least one system model coefficient based on the tire pressure signal and the tire temperature signal, and calculate the tire load of the tire using a linear system model that relates tire pressure, the duration of the contact patch, and the vehicle speed to the tire load of the tire, where the linear system model further includes the at least one system model coefficient for calculating the tire load of the tire.

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 load balance system including first and second load indicators visible outside of a vehicle having a bed, first and second load sensors configured to sense respective first and second load weights in respective first and second bed areas. The load balance system includes a controller in communication with the first and second load sensors and the first and second load indicators. The controller is configured to activate the first and second load indicators to generate the same first and second outputs in response to detection of respective first and second load weights that are less than a predetermined threshold value. The controller is configured to activate the first and second load indicators to generate different first and second outputs in response to the detection of respective first and second load weights when one of the first and the second load weights is above the predetermined threshold value.

Mappings of keys to actions can automate various vehicle systems. Some automations can provide lane departure warnings. Keys for lane departure mappings can specify vibration patterns expected when a vehicle drives over lane delineators. These vibration-based mappings can include keys with vibration patterns, e.g., defining vibration frequencies or vibration locations. Keys for emergency light mappings can be based on conditions such as (1) the vehicle being on the road, stopped, not in traffic, and not at a stop signal; (2) components of the vehicle having failed; or (3) weather conditions.