The disclosure is generally directed to vehicle load balancing systems and methods. An example method executed by a processor includes receiving, from a weight sensor, a first weight measurement of a cargo item placed upon a cargo bed of a vehicle and further includes receiving a second weight measurement from the weight sensor after detecting that the vehicle is in motion. The processor then determines, based on detecting a difference between the first weight measurement and the second weight measurement that the cargo item has shifted from a first spot to a second spot on the cargo bed. The example method can further involve placing the cargo bed in a tilted configuration followed by configuring the vehicle to jerk sporadically while traveling in a first direction that is selected to shift the cargo item back from the second spot to the first spot on the cargo bed.

A method for downforce control includes receiving vehicle inputs. The method includes determining a first normal-force request at the front axle and a second normal-force request at the rear axle using the purality of vehicle inputs and a prediction model. The prediction model is a combined state space model that integrates a half-car state space model and an actuator state space model, the half-car state space model is developed using a half-car model, and the actuator state space model is developed using a neural network model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the first normal-force request and the second normal-force request, respectively.

According to an aspect, there is provided a computer-implemented method for condition monitoring of a vehicle. The method comprises applying a dynamic model associated with a vehicle (800), the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle (200), obtaining, based on vehicle identity information, vehicle dynamics information (202), obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data (204), analyzing behavior of the vehicle based on the status information and the vehicle dynamics information (206), and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data (208); analyzing historical changes in at least one calibration parameter associated with the dynamic model of the vehicle (802); analyzing effects of the three-dimensional road map associated with the roads and the road characteristics data in at least one position on the behavior of the vehicle (804); and determining, based on the analyzed historical changes and the effects, at least one change in at least one vehicle characteristic (806).

A vehicle includes a body including suspension components, multiple wheels coupled to the body, a suspension sensor coupled to one of the suspension components or at least one of said multiple wheels, a camera, a display connected to the camera to display at least part of the camera view, a processor receiving inputs from the suspension sensor, and memory coupled to the processor. The memory includes a program from which an actual horizontal wheel position relative to a path of travel of the vehicle is determined as a function of a vertical position of the at least one of said multiple wheels. And the processor causes an image representative of the actual horizontal wheel position to appear on the display, and wherein vertical is in the direction of gravity and horizontal is perpendicular to the direction of gravity.

A method comprises: determining that a vehicle is at a first location; controlling motion of the vehicle using an advanced driver assistance system (ADAS); determining a second location for the vehicle based on receiving a first sensor return indicating at least a first static object and a second static object; determining a third location for the vehicle based on a heading angle of the vehicle and a first travel distance of the vehicle since the first location; determining a fourth location for the vehicle by fusing the second and third locations with each other, and controlling the motion of the vehicle using the ADAS based on the fourth location.

Disclosed is a method for monitoring vehicle overload based on gravity anomaly, which includes the following steps of: setting a plurality of measuring positions on a single lane, arranging a gravimeter to acquire gravity anomaly values caused by a vehicle when the middle position along the length direction of the vehicle reaching each measuring position, using a monitoring camera to judge the category of the vehicle, acquire three geometric dimensions of the vehicle, and determine the position of the vehicle. The vehicle is simplified as a cuboid, and the mass density distribution is simplified as a piecewise constant function along the length direction of the vehicle. Calculating values of the piecewise constant function according to the gravity anomaly values, calculating a total weight of the vehicle according to the mass density distribution, comparing the total weight with the weight limit of the vehicle to judge whether the vehicle is overloaded.

An automobile cornering rollover prevention system comprises a speed controller, a wheel deflection measuring instrument mounted on a front wheel of the automobile, force sensors mounted on axis positions of four wheels, and an angular speed measuring instrument and a speed controller mounted on the front wheel of the automobile, and the wheel deflection measuring instrument, the angular speed measuring instrument and the force sensor are all electrically connected to the speed controller. The speed controller is connected to a brake system of the automobile, so that the speed can be intelligently reduced through the brake system. When a driver changes .sub.1 according to a road condition, the speed controller may calculate a critical radius in real time and then compare the speed and give a command in real time for controlling the speed, so that the speed is maintained in an ideal range.

A tire state estimation method is provided for estimating normal force, lateral force and longitudinal forces based on CAN-bus accessible sensor inputs, including deploying a normal force estimator generating the normal force estimation from a summation of longitudinal load transfer, lateral load transfer and static normal force using as inputs lateral acceleration, longitudinal acceleration and roll angle derived from the input sensor data; deploying a lateral force estimator estimating lateral force using as inputs measured lateral acceleration, longitudinal acceleration and yaw rate; and deploying a longitudinal force estimator estimating the longitudinal force using as inputs wheel angular speed and drive/brake torque derived from the input sensor data.

A method for operating an electronic brake system in a vehicle having at least two tires on an axle, wherein the vehicle has a center of gravity (SP) with a height (hSP), is disclosed. According to the method, the height (hSP) of the center of gravity (SP) is calculated and used as a parameter by the electronic brake system. An electronic control unit, an electronic brake system, and a vehicle including the same for carrying out the method are also disclosed.

A tire state estimation method is provided for estimating normal force, lateral force and longitudinal forces based on CAN-bus accessible sensor inputs, including deploying a normal force estimator generating the normal force estimation from a summation of longitudinal load transfer, lateral load transfer and static normal force using as inputs lateral acceleration, longitudinal acceleration and roll angle derived from the input sensor data; deploying a lateral force estimator estimating lateral force using as inputs measured lateral acceleration, longitudinal acceleration and yaw rate; and deploying a longitudinal force estimator estimating the longitudinal force using as inputs wheel angular speed and drive/brake torque derived from the input sensor data.