A vehicle includes a front wheel and a rear wheel, a first weight sensor for detecting an axle weight of the front wheel, a second weight sensor for detecting an axle weight of the rear wheel, and an acceleration sensor for detecting a deceleration of the vehicle. The vehicle further includes a memory for storing a weight value of the vehicle and an inter-axle distance value of the vehicle and a processor configured to control driving of the vehicle and, when braking during driving, to obtain a value of a center of gravity point based on the detected weight values, the deceleration, and inter-axle distance of the vehicle. The processor is configured to control the deceleration based on the obtained value of the center of gravity point. The vehicle includes a braking device for performing braking in response to a control command of the processor.

A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle, can receive from a data store or a graphical user interface, ranges for one or more external parameters. The computer implemented method can determine optimum values for external parameters of the vehicle by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the vehicle parameter as determined by the computer implemented method.

A method approximates a friction value between wheels of a vehicle and a road surface. The method includes the following steps: carrying out at least one test acceleration of the vehicle by acting on at least one test wheel; ascertaining a wheel slip of the test wheel for at least one period of the test acceleration; ascertaining a test manipulated variable provided during the period in order to act on the test wheel; ascertaining a test load characteristic present on the test wheel during the period; and ascertaining a reference friction value for the test acceleration on the basis of the ascertained test load characteristic, the ascertained test manipulated variable and the ascertained wheel slip of the test wheel. A driver assistance system is configured to perform the method. A vehicle includes the driver assistance system.

A vehicle control system may include a sensor network sensing vehicle attitude information and a controller operably coupled to the sensor network to determine, based on the vehicle attitude information, movement of a center of gravity of the vehicle relative to an axis of rotation of the vehicle. The controller may further determine a modification to a torque application of the vehicle based on the movement of the center of gravity of the vehicle relative to the axis of rotation of the vehicle.

A method and system are provided for identifying time-varying suspension characteristics of heavy-load vehicles. The method includes collecting sequential control state data of a mining truck using sensors, predicting parameter-related factors through a deep learning network, estimating suspension stiffness and damping coefficients via a linear dynamic model considering longitudinal-vertical coupling, and predicting future system states through a nonlinear dynamic model based on the estimated parameters and learned factors. According to the method, a deep learning network is integrated into a physical model of the mining truck, an accurate longitudinal-vertical dynamical model of the mining truck is established, accurate suspension parameters are identified, the stiffness damping time-varying characteristics of the suspension of the mining truck are given through a physical model-data driving method, and the model has certain interpretability and generalization; the rigidity and damping of the four suspensions can be obtained only through sprung information.

Disclosed is a vehicle control apparatus which includes a sensor, memory, and a controller. For example, the vehicle control apparatus may obtain, using the sensor, environmental information about a surrounding environment of a vehicle that is driving and driving information of the vehicle, determine a plurality of boundary paths for driving of the vehicle, based on at least one of the environmental information, the driving information, or a maximum drivable curvature of the vehicle, determine an expected driving path based on a curvature required for the vehicle to drive to a destination, determine, based on the expected driving path being located within the plurality of boundary paths, a steering angle for following the expected driving path, update the expected driving path based on the determined steering angle, the environmental information, and the driving information, and control the vehicle based on the updated expected driving path.

A vehicle is provided including a plurality of wheel assemblies, a body supported on the plurality of wheel assemblies and having a cargo area for receiving a cargo load, a load sensing system configured to sense a vehicle load and generate vehicle load signals indicative of the sensed vehicle load, and an object recognition system configured to detect one or more objects in the cargo area and recognize the one or more objects. The vehicle also includes a controller processing the recognized one or more objects and determining dimensions of the cargo load and an estimated center of gravity of cargo load based on the dimensions of the one or more objects in the cargo area, and generates an output indicative of the estimated center of gravity of the cargo load.

A computer implemented method for determining optimal values for operational parameters for a model predictive controller for controlling a vehicle, can receive from a data store or a graphical user interface, ranges for one or more external parameters. The computer implemented method can determine optimum values for external parameters of the vehicle by simulating a vehicle operation across the ranges of the one or more operational parameters by solving a vehicle control problem and determining an output of the vehicle control problem based on a result for the simulated vehicle operation. A vehicle can include a processing component configured to adjust a control input for an actuator of the vehicle according to a control algorithm and based on the optimum values of the vehicle parameter as determined by the computer implemented method.

A vehicle wheel force control system includes multiple vehicle wheel height sensors, a vehicle IMU configured to measure lateral and longitudinal acceleration, and a vehicle control module is configured to obtain vehicle wheel height parameters from the multiple vehicle wheel height sensors, obtain a vehicle lateral and longitudinal acceleration parameters from the vehicle IMU, estimate a pitch arm rotation value of the vehicle, determine a pitch center of the vehicle based on the pitch arm rotation value, the vehicle wheel height parameters and the vehicle lateral acceleration parameter and the vehicle longitudinal acceleration parameter, calculate a center of gravity of the vehicle according to the determined pitch center, determine an estimated normal force on at least one wheel of the vehicle based, at least in part, on the calculated center of gravity of the vehicle, and control operation of at least one vehicle component according to the estimated normal force.

A method for operating a self-driving working machine including determining an operating variable of a working device of the self-driving working machine and determining a longitudinal dynamics driving variable of the self-driving working machine. The method also includes checking whether a hazardous state for stability of the self-driving working machine can occur based on the determined operating variable and the determined longitudinal dynamics driving variable The method also includes intervening in longitudinal dynamics of the self-driving working machine depending on a check result based on the checking step.