A method for controlling movement of an autonomous vehicle includes: estimating a set of tire force margins with respect to each wheel of the vehicle and a set of dynamic chassis margins associated with a chassis module of the vehicle; based on the margins, determining whether the vehicle is capable of moving in accordance with a decision command; when the determination is negative, outputting a request for update of the decision command; and when an updated decision command has not been received, calculating a set of marginal actuating signals based on the decision command, the margins, required slip angles respectively for the wheels and a center of percussion of the vehicle so as to make the autonomous vehicle move accordingly.

A control unit (130, 140) for controlling a heavy duty vehicle (100), wherein the control unit is arranged to obtain input data indicative of a desired wheel force (Fx, Fy) to be generated by at least one wheel (210) of the vehicle (100), and to translate the input data into a respective equivalent wheel speed or wheel slip to be maintained by the wheel (210) to generate the desired wheel force (Fx, Fy) based on an inverse tyre model (f.sup.−1) for the wheel (210), wherein the control unit (130, 140) is arranged to obtain the inverse tyre model in dependence of a current operating condition of the wheel (210), and wherein the control unit (130, 140) is arranged to control the heavy duty vehicle (100) based on the equivalent wheel speed or wheel slip.

A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

A vehicle motor control apparatus includes: a processor configured to determine whether a state of a vehicle is an over-steer state or an under-steer state, to determine a driving control mode or a braking control mode of a motor based on a determination result of the state of the vehicle, to calculate a target yaw moment of based on a tire force by using the over-steer state or the under-steer state, and to determine a motor control amount that follows the target yaw moment; and a storage configured to store data and algorithms driven by the processor.

A turning controller for a vehicle is configured to execute: a time obtaining process that obtains collision prediction time; a lateral movement amount determining process that determines whether a target lateral movement amount is greater than or equal to a lateral movement amount determination value; and an automatic turning process that, in a case in which the collision prediction time is shorter than or equal to a determination prediction time, outputs a command for steering the front wheel to the front wheel steering device and outputs a command for steering the rear wheel to the rear wheel steering device, in order to avoid a collision between the vehicle and the obstacle; a counter-phase process in the automatic turning process in a case in which the target lateral movement amount is determined to be greater than or equal to the lateral movement amount determination value.

A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

A system includes inertial sensors and a GPS. The system generates a first estimated vehicle velocity based on motion data and positioning data, generates a second estimated vehicle velocity based on the processed motion data and the first estimated vehicle velocity, and generates fused datasets indicative of position, velocity and attitude of a vehicle based on the processed motion data, the positioning data and the second estimated vehicle velocity. The generating the second estimated vehicle velocity includes: filtering the motion data, transforming the filtered motion data in a frequency domain based on the first estimated vehicle velocity, generating spectral power density signals, generating an estimated wheel angular frequency and an estimated wheel size based on the spectral power density signals, and generating the second estimated vehicle velocity as a function of the estimated wheel angular frequency and the estimated wheel size.

A method for determining a slip angle λ.sub.r of a rear wheel of a single-track motor vehicle for the purpose of traction control of the rear wheel of the single-track motor vehicle by means of a closed loop control Is provided. The slip angle λ.sub.r of the rear wheel is determined as a feedback value of the closed loop using at least one of three model-based steps. A slip angle λ.sub.r1, λ.sub.r2 or λ.sub.r3 is determined by one of the three steps representing the slip angle λ.sub.r or the slip angle λ.sub.r is determined from at least two of the slip angles λ.sub.r1, λ.sub.r2 and λ.sub.r3.

A method for determining a slip angle λ.sub.r of a rear wheel of a single-track motor vehicle for the purpose of traction control of the rear wheel of the single-track motor vehicle by means of a closed loop control Is provided. The slip angle λ.sub.r of the rear wheel is determined as a feedback value of the closed loop using at least one of three model-based steps. A slip angle λ.sub.r1, λ.sub.r2 or λ.sub.r3 is determined by one of the three steps representing the slip angle λ.sub.r or the slip angle λ.sub.r is determined from at least two of the slip angles λ.sub.r1, λ.sub.r2 and λ.sub.r3.