A method in which the position of the center of gravity of a moving motor vehicle is ascertained, wherein at least one set of related input variables is taken into consideration, and the set of input variables includes at least a longitudinal acceleration of the motor vehicle, a lateral acceleration of the motor vehicle, a yaw rate of the motor vehicle and at least one wheel rotational speed, in particular four wheel rotational speeds, wherein the set of input variables is ascertained during a steady-state driving maneuver, and a quantity of possible center of gravity positions is defined as classes and, by a learning-based classification method, on the basis of the set of input variables, a class is selected which indicates an estimated center of gravity position. A control unit for carrying out the method is also disclosed.

A flood sensing device includes: an acquiring section that acquires plural types of traveling state data relating to traveling of a vehicle; and a sensing section that senses flooding of a road on which the vehicle travels by using (a) a physical quantity that is estimated on the basis of (i) a vehicle behavior model, which is configured by driving force of the vehicle and traveling resistance that includes air resistance applied to the vehicle, slope resistance applied to the vehicle and rolling resistance applied to the vehicle, and which determines the physical quantity that varies due to the vehicle traveling, and (ii) the plural types of traveling state data of the present time that are acquired by the acquiring section, and (b) the physical quantity that is obtained from the traveling state data of the present time that is acquired by the acquiring section.

The techniques of this disclosure relate to a vehicle lateral-control system with dynamically adjustable calibrations. The system includes a controller circuit that receives weather data for a geographic region traveled by a vehicle. The controller circuit receives image data from a camera of a roadway traveled by the vehicle in the geographic region. The controller circuit receives vehicle-state data from one or more vehicle sensors indicating an operating condition of the vehicle on the roadway in the geographic region. The controller circuit adjusts a calibration of vehicle lateral-control features based on the weather data, the image data, and the vehicle-state data. The controller circuit operates the vehicle on the roadway based on the adjusted calibration. The system can increase passenger comfort or reduce an error from a lane centerline when the vehicle is operated in an autonomous-driving mode, which can improve operational safety.

Motorcycles can become unstable when operating at high speeds and at high cornering levels. For example, they can exhibit an oscillation at the rear wheel commonly known as “weave.” A system and method is provided which utilizes a high-fidelity computer simulation model of a 2- or 3-wheel motorcycle to predict operating states such as yaw rate, lateral acceleration and roll angle for a stable motorcycle at a given speed and steer angle. The operating state of a physical motorcycle can be measured and compared to that of the model at every instant in time to determine if the operating state of the physical motorcycle differs from that of the simulation model in such a way as to indicate loss of stability. The nature of that difference can then be used to intervene in the operation of the motorcycle independent of driver actions by application of brakes, modulating the engine torque or applying torques to urge the steering system in a corrective direction. Thus by comparing the physical response of the motorcycle to that of the computer model in an on-board controller these interventions can be applied at a time and intensity to stabilize the motorcycle and prevent a loss of control.

An apparatus comprising an interface, a memory and a processor. The interface may be configured to receive sensor data samples during operation of a vehicle. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to analyze the sensor data samples stored in the memory to detect a pattern. The processor may be configured to manage an application of brakes of the vehicle in response to the pattern.

A universal modeling method is provided for a motor vehicle, the universal modeling method including: providing an input signal set, the input signal set comprising those signals of respective sensors of the motor vehicle which can be relevant for the control of devices of the motor vehicle; selecting a set of modeling signals from the input signal set as a function of a system architecture of the motor vehicle; and determining an output signal set by way of a time-discrete selective state space model modeling function taking into account the set of modeling signals. In this case, the output signal set functions as a signal set for controlling corresponding actuators of the devices of the motor vehicle.

A vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle.

A system and a method are configured to control a traction force of a vehicle, for example, an electrified vehicle. The system includes wheel speed sensors mounted on drive wheels, respectively, of the vehicle to measure a drive wheel speed, a disturbance observer for extracting a primary disturbance by comparing an actual vehicle behavior based on a required torque with a vehicle behavior estimated based on the drive wheel speed using a vehicle behavior model in an acceleration situation of the vehicle, a filter for extracting a secondary disturbance in a preset frequency range from the primary disturbance, a compensator for calculating a compensation torque for cancelling the secondary disturbance, a hysteresis circuit for determining whether to compensate for the required torque based on the compensation torque, and a calculator for calculating a compensated required torque using the required torque and the compensation torque.

In some embodiments, techniques for updating a model that represents turning dynamics of a vehicle are provided. In some embodiments, an initial location and orientation of the vehicle with respect to an object outside the vehicle are determined using information from environment sensors including but not limited to image sensors and/or range sensors. In some embodiments, vehicle state information including but not limited to steering angle and wheel speed is received before determining a new location and orientation of the vehicle with respect to the object. In some embodiments, the model is updated using the initial location and orientation, the new location and orientation, and the vehicle state information.

A vehicle and a method of controlling turning thereof are provided. The turning control method of a vehicle includes calculating first compensation torque based on a lateral acceleration variation during turning, determining first compensated demanded torque by applying the first compensation torque to demanded torque, determining second compensation torque for preventing wheel slip of a driving wheel based on the first compensated demanded torque and an actual vehicle behavior, and determining second compensated demanded torque input to a driving source controller by applying the second compensation torque to the first compensated demanded torque.