A method for integrated control of handling stability of a distributed drive electric vehicle is provided, in which a Magic Formula tire model is subjected to piecewise linear fitting to obtain a piecewise affine tire model; a hybrid logical dynamic model is established based on the piecewise affine tire model; a hierarchical integrated control strategy is adopted to obtain an upper-layer hybrid model predictive controller and a lower-layer four-wheel torque optimal allocation controller, so as to calculate an additional yaw moment, an additional front-wheel steering angle and a wheel drive torque. Related devices for implementing the integrated control method are also provided.

A method for estimating a lateral velocity of a vehicle includes receiving sensor data from a sensor of the vehicle, determining a physics-based longitudinal velocity estimation of the vehicle using a physics-based model and the sensor data, determining a data-driven longitudinal velocity estimation of the vehicle using a first neural network and the sensor data, determining, using a second neural network, which of the physics-based longitudinal velocity estimation and the data-driven longitudinal velocity estimation is more reliable to determine a selected longitudinal velocity estimation, determining the lateral velocity of the vehicle using the selected longitudinal velocity estimation, and controlling the vehicle based on the lateral velocity.

Aspects of the present invention relate to a control system and to a method of controlling a total driven wheel torque for a vehicle by controlling torque output of a first torque source of the vehicle and of a second torque source of the vehicle, wherein the first torque source is configured to provide drive torque to a first axle of the vehicle for generating first axle wheel torque, wherein the second torque source is configured to provide drive torque to a second axle of the vehicle for generating second axle wheel torque, the method comprising: receiving a total torque request for total driven wheel torque; producing a first torque request for the first torque source and a second torque request for the second torque source, in dependence on the total torque request for the total driven wheel torque; and when at least one of the first and second torque requests is not satisfiable, modifying at least one of the first and second torque requests to enable a sum of the first axle wheel torque and the second axle wheel torque to approach or satisfy the total torque request, wherein the modification of at least one of the torque requests is controlled by at least one torque rate modifier configured to increase or decrease a rate of change of at least one of the torque requests.

In a factory in which a plurality of processes for manufacturing a vehicle that travels via unmanned driving is performed, a monitoring device monitors the vehicle that is an object of the processes. The monitoring device includes a process acquisition unit, a torque acquisition unit, and a detection unit that detects an abnormality in an output torque of the vehicle by using process information and torque information.

A method for estimating a lateral velocity of a vehicle includes receiving sensor data from a sensor of the vehicle, determining a physics-based longitudinal velocity estimation of the vehicle using a physics-based model and the sensor data, determining a data-driven longitudinal velocity estimation of the vehicle using a first neural network and the sensor data, determining, using a second neural network, which of the physics-based longitudinal velocity estimation and the data-driven longitudinal velocity estimation is more reliable to determine a selected longitudinal velocity estimation, determining the lateral velocity of the vehicle using the selected longitudinal velocity estimation, and controlling the vehicle based on the lateral velocity.

A vehicle includes a powerplant, an accelerator pedal, and a wheel driven by the powerplant. A vehicle controller is programmed to, while a position of the accelerator pedal is constant and responsive to cessation of slip of the driven wheel due to the driven wheel transitioning from a first surface to a second surface, command torque from the powerplant such that the torque increases at a rate that depends on a last learned value of a coefficient of friction of the first surface at the transitioning.

A mining machine includes: a road gradient calculator that calculates a road gradient of a travel route based on a position and a speed measured by a GNSS receiver, a vehicle body posture measured by a vehicle body posture sensor, and an acceleration measured by an acceleration sensor; a traction coefficient calculator that calculates a traction coefficient based on the speed measured by the GNSS receiver, the acceleration measured by the acceleration sensor, a wheel speed measured by a wheel speed sensor, a steering direction measured by a steering angle sensor, a vehicle weight measured by a load sensor, and a driving torque measured by a driving torque sensor; and a target torque calculator that calculates a target torque based on the road gradient calculated by the road gradient calculator and the traction coefficient calculated by the traction coefficient calculator.

A method and a device are disclosed for assisting with the lateral positioning of a vehicle, said vehicle being able to be driven by a driver in an automated manner along a reference path in a traffic lane, said traffic lane being bounded by two edges. The method comprises steps of detecting an upcoming split in the traffic lane, determining a widened area in the traffic lane, and determining a plurality of reference paths.

A computer-implemented method performed in a vehicle control unit for controlling motion of a heavy-duty vehicle. The method includes obtaining a vehicle motion request, wherein the vehicle motion request is indicative of a target curvature and a target acceleration, determining a motion support device, MSD, control allocation based on the vehicle motion request, determining a dynamic wheel slip angle limit based on the vehicle motion request, where dynamic wheel slip angle limit increases with a decreasing target acceleration, and controlling the motion of the heavy-duty vehicle based on the MSD control allocation constrained by the dynamic wheel slip angle limit.

The present disclosure provides a method and a device for vehicle parking control. The method includes following steps performed according to a predetermined time period until the vehicle stops at an end point: determining (101) a target position and a target speed when the vehicle arrives at the target position based on a current speed of the vehicle and a distance between a current position and the end point, the target position being on a road where the vehicle is located and in front of the vehicle; determining (102) a deceleration motion mode for the vehicle based on the current speed of the vehicle and the target speed; and performing (103) braking control for the vehicle in accordance with a vehicle braking strategy corresponding to the deceleration motion mode. The method can solve the problem in the related art associated with inaccurate vehicle parking control and uncomfortable experience.