A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

Methods and systems implemented in a vehicle involve obtaining a single camera image from a camera arranged on the vehicle. The image indicates a heading angle Ψ.sub.0 between a vehicle heading x and a tangent line that is tangential to road curvature of a road on which the vehicle is traveling and also indicates a perpendicular distance y.sub.0 from a center of the vehicle to the tangent line. An exemplary method includes obtaining two or more inputs from two or more vehicle sensors, and estimating kinematic states of the vehicle based on applying a Kalman filter to the single camera image and the two or more inputs to solve kinematic equations. The kinematic states include roll angle and pitch angle of the vehicle.

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.

Fuzzy logic based and machine learning enhanced vehicle dynamics determination is provided. A system can identify a previous longitudinal velocity and receive data from an inertia measurement unit. The system can determine a roll angle and a pitch angle. The system can determine a lateral acceleration and a longitudinal acceleration. The system can receive wheel speed sensor data, tire pressure sensor data, and steering angle sensor data, and use the data to determine a longitudinal velocity. The system can select one of a reduced-order non-linear Luenberger observer technique or a reduced-order Kalman filter technique. The system can determine a lateral velocity and a sideslip angle. The system can provide the lateral velocity and the sideslip angle to a vehicle controller.

A yaw motion control method for a four-wheel distributed vehicle includes: calculating the steering response of the vehicle in a steady state using a nonlinear vehicle model in reference with an understeering degree while constraining by the limit value of the road surface adhesion condition according to the sideslip angle response and the vertical load change in the steady state, calculating the lateral force response and the self-aligning moment response of the tires in the steady state by a magic tire formula, calculating the required additional yaw moment by using the yaw motion balance equation, reasonably distributing the generalized control force to the four drive motors through the optimization algorithm in combination with the current driving conditions; finally, off-line storing and retrieving the calculation results of the off-line distribution of different vehicle parameters required by different upper layers to distribute the torques to the four drive wheels.

Methods and systems are provided for inducing vehicle side slip. In one example, a method comprises opening a sole driveline disconnect clutch positioned between an engine and an electric machine upstream of a transmission, and closing the sole driveline disconnect clutch within a predetermined amount of time after opening the sole driveline disconnect without shifting gears of the transmission. In this way, vehicle drift may be reliable initiated in a hybrid electric vehicle with an automatic transmission, without shifting gears of the transmission.

Methods and systems are provided for inducing vehicle side slip. In one example, a method comprises opening a sole driveline disconnect clutch positioned between an engine and an electric machine upstream of a transmission, and closing the sole driveline disconnect clutch within a predetermined amount of time after opening the sole driveline disconnect without shifting gears of the transmission. In this way, vehicle drift may be reliable initiated in a hybrid electric vehicle with an automatic transmission, without shifting gears of the transmission.

A vehicle and method for controlling the same are provided. The vehicle includes a speed detector that detects driving speed of the vehicle, a detection sensor that obtains information regarding at least one of a position and a speed of an object around the vehicle, and a yaw rate detector that detects a speed at which a rotation angle of the vehicle's frame is changed while the vehicle is driven. A controller then determines a yaw rate required for the vehicle to steer to avoid the object, applies partial braking on an inner wheel of the vehicle based on the determined yaw rate, and applies partial braking on an outer wheel of the vehicle to reduce a beta value of the vehicle obtained during the steering-based avoidance when the beta value exceeds a predetermined value.

A traveling control system includes a first acquisition unit, a second acquisition unit, a path plan setter and a controller. The first a acquisition unit communicates with a cloud environment including an edge environment to acquire cloud information including traveling information of other vehicles from at least the edge environment. The second acquisition unit recognizes a traveling environment of an own vehicle and acquires own vehicle traveling information including the recognized traveling environment and a vehicle control state of the own vehicle. The path plan setter sets a path plan in automatic driving control to cause the own vehicle to travel automatically. The determines a need for changing the path plan and the automatic driving control based on the cloud information and controls traveling behavior of the own vehicle according to the need for a change based on the cloud information and the own vehicle traveling information.