Disclosed herein are an apparatus and method for compensating for a heading angle. The apparatus for compensating for a heading angle includes a compensation condition determination unit configured to determine whether a predetermined compensation condition is satisfied, to compensate for a heading angle of a camera, and a heading angle processing unit configured to compensate for the heading angle of the camera using a lane distance input from the camera, when it is determined by the compensation condition determination unit that the compensation condition is satisfied.

A vehicle control apparatus comprising, a center of gravity six-component calculation unit for calculating a center of gravity six-component as vehicle motion targets based on a driver input, a tire three-component calculation unit for calculating a tire three-component of four wheels of a vehicle based on the center of gravity six-component, a vehicle control unit for performing vehicle control by the vehicle control, actuator group based on the tire three-component of the four wheels, and wherein the tire three-component calculation unit calculates the tire three-component of the four wheels from the center of gravity six-component by a coordinate transformation without repetition, which is normalization with the driving stiffness of each wheel and the cornering stiffness of each wheel, when the number of control requests in the vehicle control is less than degrees of freedom of the vehicle control actuator group.

A driving assistance apparatus includes a processor having hardware. The processor is configured to acquire vehicle speed data before an ABS of a vehicle is activated and vehicle speed data when the ABS of the vehicle is stopped, calculate a coefficient of sliding friction based on the vehicle speed data before the ABS is activated and the vehicle speed data when the ABS is stopped, determine whether the coefficient of sliding friction is equal to or smaller than a threshold, and detect that a slip due to road freezing has occurred when the coefficient of sliding friction is equal to or smaller than the threshold.

A driving assistance apparatus includes a processor having hardware. The processor is configured to acquire vehicle speed data before an ABS of a vehicle is activated and vehicle speed data when the ABS of the vehicle is stopped, calculate a coefficient of sliding friction based on the vehicle speed data before the ABS is activated and the vehicle speed data when the ABS is stopped, determine whether the coefficient of sliding friction is equal to or smaller than a threshold, and detect that a slip due to road freezing has occurred when the coefficient of sliding friction is equal to or smaller than the threshold.

This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.

This disclosure relates to method and system for detecting and compensating for mechanical fault in autonomous ground vehicle (AGV). For each of a set of trajectory plan segments along a base path during real-time navigation of the AGV, the method may include receiving a plurality of vehicle displacement parameters along a given trajectory plan segment. and determining an optimal velocity twist of the AGV in the given trajectory plan segment using an artificial intelligence (AI) model, based on the plurality of vehicle displacement parameters and a weight of the AGV. The method may further include determining the mechanical fault in the AGV based on a comparison of an actual velocity twist of the AGV in the given trajectory plan segment and the optimal velocity twist of the AGV in the given trajectory plan segment for each of the set of trajectory plan segments.

Disclosed herein are an apparatus and method for compensating for a heading angle. The apparatus for compensating for a heading angle includes a compensation condition determination unit configured to determine whether a predetermined compensation condition is satisfied, to compensate for a heading angle of a camera, and a heading angle processing unit configured to compensate for the heading angle of the camera using a lane distance input from the camera, when it is determined by the compensation condition determination unit that the compensation condition is satisfied.

Disclosed herein are an apparatus and method for compensating for a heading angle. The apparatus for compensating for a heading angle includes a compensation condition determination unit configured to determine whether a predetermined compensation condition is satisfied, to compensate for a heading angle of a camera, and a heading angle processing unit configured to compensate for the heading angle of the camera using a lane distance input from the camera, when it is determined by the compensation condition determination unit that the compensation condition is satisfied.

A method is provided for steering control of a vehicle by using lateral velocity of two know points (or lateral velocity of one known point and yaw rate), longitudinal velocity and steer angle information. These factors are used to calculate a target steer angle based on the track angle based on yaw decomposition to thus adjust a current steer angle of the vehicle based on the target steer angle.

A method is provided for steering control of a vehicle by using lateral velocity of two know points (or lateral velocity of one known point and yaw rate), longitudinal velocity and steer angle information. These factors are used to calculate a target steer angle based on the track angle based on yaw decomposition to thus adjust a current steer angle of the vehicle based on the target steer angle.