An illustrative example method is for estimating a friction characteristic of a surface beneath a vehicle that has a plurality of wheels contacting the surface. The method includes determining a wheel speed of at least one of the wheels, determining a velocity of the at least one of the wheels separately from determining the wheel speed, determining a wheel slip of the at least one of the wheels based on the determined wheel speed and the determined velocity, and determining the friction characteristic based on the determined wheel slip. Determining the velocity separately from the wheel speed is accomplished using at least one detector that provides an output corresponding to a range rate, such as a RADAR or LIDAR detector.

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.

An illustrative example method is for estimating a friction characteristic of a surface beneath a vehicle that has a plurality of wheels contacting the surface. The method includes determining a wheel speed of at least one of the wheels, determining a velocity of the at least one of the wheels separately from determining the wheel speed, determining a wheel slip of the at least one of the wheels based on the determined wheel speed and the determined velocity, and determining the friction characteristic based on the determined wheel slip. Determining the velocity separately from the wheel speed is accomplished using at least one detector that provides an output corresponding to a range rate, such as a RADAR or LIDAR detector.

Methods, apparatuses, and computer-readable media are described. In one example, a method of controlling a vehicle comprises: receiving, using one or more sensors, a first set of measurements of a set of physical attributes of the vehicle in a motion; determining, based on a motion data model that defines a set of relationships among the set of physical attributes of the vehicle in the motion and based on the first set of measurements, a set of expected measurements of the set of physical attributes; determining whether to use an entirety of the first set of measurements to control an operation of the vehicle based on comparing the first set of measurements and the set of expected measurements; and responsive to determining not to use the entirety of the first set of measurements, controlling the operation of the vehicle based on a second set of measurements.

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.

A travel control apparatus (140) includes a acquisition unit (141), which acquires information on a target track through which a vehicle (M) will pass in the future, a parameter determiner (142), which determines a parameter by which a track model defined by one or more parameters coincides with a track acquired by the acquisition unit, and a steering controller (143), which feedforward-controls steering of the vehicle on the basis of at least the track model defined by the parameter determined by the parameter determiner, wherein the parameter determiner determines the parameter on the basis of a direction of a change in the degree of separation between the track model and the target track with respect to a change in the parameter.

A system for controlling a motion of a vehicle from an initial state to a target state includes a path planner to determine a discontinuous curvature path connecting the initial state with the target state by a sequential composition of driving patterns. The discontinuous curvature path is collision-free within a tolerance envelope centered on the discontinuous curvature path. The system further includes a path transformer to locate and replace at least one treatable primitive in the discontinuous curvature path with a corresponding continuous curvature segment to form a modified path remaining within the tolerance envelope. Each treatable primitive is a predetermined pattern of elementary paths. The system further includes a controller to control the motion of the vehicle according to the modified path.

Methods, apparatuses, and computer-readable media are described. In one example, a method of controlling a vehicle comprises: receiving, using one or more sensors, a first set of measurements of a set of physical attributes of the vehicle in a motion; determining, based on a motion data model that defines a set of relationships among the set of physical attributes of the vehicle in the motion and based on the first set of measurements, a set of expected measurements of the set of physical attributes; determining whether to use an entirety of the first set of measurements to control an operation of the vehicle based on comparing the first set of measurements and the set of expected measurements; and responsive to determining not to use the entirety of the first set of measurements, controlling the operation of the vehicle based on a second set of measurements.

A motion of a vehicle is controlled according to a sequential compositions of the elementary paths following a transformation of one of a first pattern, a second pattern, and a third pattern. These three patterns are predetermined and form an exhaustive set of patterns. The three patterns are represented by corresponding functions stored in a memory. The functions representing the patterns are used, in response to receiving an initial state and a target state of the vehicle, to determine parameters of the minimum-curvature path. The motion of the vehicle is controlled according to the parameters of the minimum-curvature path.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.