A method of controlling a vehicle having wheels provided with tires resting on a surface, the method using a model of the physical behavior of each tire as a function of a sideslip angle (β.sub.ij) for each tire relative to the surface. The model is obtained by implementing an adaptive algorithm that selectively applies an affABREGEine model (Z1), a DUGOFF model (Z2), or a constant model (Z3).

A vehicle and a system and method of controlling the vehicle. The system includes a sensor and a processor. The sensor obtains a first estimate of a force on a tire of the vehicle based on dynamics of the vehicle. The processor is configured to obtain a second estimate of the force on the tire using a tire model, determine an estimate of a coefficient of friction between the tire and the road from the first estimate of the force and the second estimate of the force, and control the vehicle using the estimate of the coefficient of friction.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, with the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19). Additionally, the processor unit (3) is configured to optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), where the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15) to be minimized.

Simulated degraded sensor data may be generated for use in training a model. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received and converted into the simulated degraded sensor data for a particular degrading condition, such as a weather-related degrading condition. Then, the simulated degraded sensor data may be used to train a model for evaluating performance of the perception system to detect objects external to the autonomous vehicle under one or more conditions.

An arithmetic model generation system includes a sensor information acquisition unit, a tire force calculator, and an arithmetic model update unit. The sensor information acquisition unit acquires acceleration of a tire. The tire force calculator includes an arithmetic model for calculating tire force F based on the acceleration, and calculates the tire force F by inputting the acceleration acquired by the sensor information acquisition unit. The arithmetic model update unit compares tire axial force measured by the tire and the tire force F calculated by the tire force calculator, and updates the arithmetic model.

Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV). A control input is received from an ADV perception and planning system. The control input is translated in a control command to a control subsystem of the ADV. A reference actuation output is obtained from a storage of the ADV. The reference actuation output is a smoothed output that accounts for second order actuation dynamic delays attributable to the control subsystem actuator. Based on a difference between the control input and the reference actuation output, adaptive gains are determined and applied to the input control signal to reduce error between the control output and the reference actuation output.

A system includes a battery, an engine, and a processor. The processor is configured to plan, according to a model, an activation action of the engine of a vehicle for a next road segment subsequent to a current road segment; and activate, for the next road segment, the engine according to the activation action. The model includes a state space that includes a navigation map, which includes the current road segment of the vehicle, a current charge level of the battery, and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

A method for adaptive estimation of a road surface adhesion coefficient for a vehicle with complex excitation conditions taken into consideration comprises the following steps: 1) designing an estimator according to a single-wheel dynamics model of a vehicle, and estimating a longitudinal tire force and a road surface peak adhesion coefficient under longitudinal excitation; 2) designing an estimator according to a two-degree-of-freedom kinematic model of the vehicle, and estimating a tire aligning moment and a road surface peak adhesion coefficient under excitation of a lateral force; and 3) determining an excitation condition met by the vehicle according to a vehicle state parameter, performing fuzzy inference to obtain limits achievable by current longitudinal and lateral tire forces, and designing a fusion observer to fuse estimation results. The method achieves favorable robustness, improves real-time capability, and can be performed quickly and accurately.

A method for determining a tread depth of a tread of a tire during operation of a vehicle having the tire, a control device for a vehicle for determining a tread depth of a tread of a tire of the vehicle, and a system for a vehicle having such a control device and at least one electronic wheel unit, are provided. Provision is made to determine the tread depth based on a determined instantaneous dynamic wheel radius of a wheel, having the tire, of the vehicle and a determined instantaneous dynamic inside radius of the tire. In addition, at least one further first operating parameter of the tire, selected from the group including an instantaneous roadway gradient, an instantaneous vehicle drive mode and an instantaneous tire material expansion, is determined and taken into consideration.