Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

A control system for a powertrain of a vehicle includes a set of sensors configured to monitor a set of operating parameters of the vehicle indicative of at least (i) whether a driver of the vehicle is in control of the vehicle, (ii) an intended direction of motion of the vehicle, and (iii) actual motion of the vehicle and a controller configured to, based on the set of operating parameters determine whether the driver of the vehicle is in control of the vehicle and when the driver is determined not to be in control of the vehicle determine whether the actual motion of the vehicle is in the intended direction of motion of the vehicle and when the actual motion of the vehicle is not in the intended direction of motion of the vehicle, control a torque output of the engine to hold the vehicle stationary.

Systems, methods, and other embodiments described herein relate to adaptively selecting a controller for generating vehicle controls. In one embodiment, a method includes, in response to acquiring sensor data about a surrounding environment of the vehicle, determining a driving context of the vehicle in relation to aspects of a roadway on which the vehicle is traveling. The method includes selecting a controller for generating control inputs to the vehicle according to the driving context by selecting between a proportional, integral, derivative (PID) controller and a machine learning (ML) controller. The method includes controlling the vehicle using the controller.

Systems and methods for controlling a vehicle are provided. The systems and methods provide a vehicle dynamics model that relates at least one input vehicle dynamics variable to at least one output vehicle dynamics variable. The systems and methods detect a crosswind impacting the vehicle by detecting a disturbance associated with the vehicle dynamics model caused by the crosswind and adapt control of the vehicle based on the detecting the crosswind impacting the vehicle.

A driving assist system for a vehicle includes a sensor disposed at the equipped vehicle and having a field of sensing exterior of the equipped vehicle and forward of the equipped vehicle. A controller includes a processor operable to process data captured by the sensor. The controller, responsive at least in part to an initial speed setting of an adaptive cruise control system of the equipped vehicle, controls acceleration of the equipped vehicle. The controller, responsive at least in part to processing by the processor of data captured by the sensor, determines presence of a target vehicle ahead of the equipped vehicle and determines an acceleration profile to adjust the speed of the vehicle from the current vehicle speed to a target speed. The controller adjusts the acceleration of the equipped vehicle responsive to the acceleration profile, which has smooth transitions between the initial speed setting and the target speed.

A method for regulating a kinematic variable of a motor vehicle. The method includes: receiving actual value signals, which represent an actual value of a kinematic variable of a motor vehicle; receiving setpoint signals, which represent a setpoint value of the kinematic variable; ascertaining an actuating variable to be implemented by one or multiple actuating element(s) of the motor vehicle, based on the actual value, the setpoint value and a variation of the setpoint value over time in such a way that a deviation between the actual value and the setpoint value becomes smaller when the actuating variable is implemented with the aid of the one or multiple actuating element(s); and outputting actuating variable signals, which represent the ascertained actuating variable. A device, a computer program, and a machine-readable memory medium are also described.

A vehicle control device includes a controller configured to control operation of a driving motor that is to output a driving force for a vehicle. The controller is switchable between a normal mode of controlling acceleration/deceleration based on a driver's acceleration/deceleration operation, and a cruise control mode of maintaining the vehicle speed at a target speed without the acceleration/deceleration operation. The controller is configured to, during the cruise control mode, calculate a torque command value for the motor by using integral control based on an integrated value of a deviation between the vehicle speed and the target speed, and execute an integrated-value adjustment process if the controller determines that the vehicle entered a flat road or an uphill road from a downhill road or a downhill road from a flat road or an uphill road. The process adjusts the integrated value to reduce an absolute value of the integrated value.

A mobile object according to an embodiment of the present technology includes an acquisition unit, a detection unit, and a determination unit. The acquisition unit acquires situation information regarding a situation of the mobile object. The detection unit detects an instability element for autonomous traveling control of the mobile object on the basis of the acquired situation information. The determination unit determines a control method for executing the autonomous traveling control on the basis of the detected instability element.

An automotive vehicle includes an actuator configured to control vehicle steering, a sensor configured to detect a yaw rate of the vehicle, and a controller. The controller is configured to estimate a yaw rate and lateral velocity of the vehicle via a vehicle dynamics model based on a measured longitudinal velocity of the vehicle, calculated road wheel angles of the vehicle, and estimated tire slip angles of the vehicle. The controller is configured to receive a measured yaw rate from the sensor, and to calculate a difference between the measured yaw rate and the estimated yaw rate. The controller is configured to apply a model correction to the vehicle dynamics model using a PID controller based on the difference, and to estimate a vehicle position based on the estimated lateral velocity and the measured longitudinal velocity. The controller is configured to automatically control the actuator based on the vehicle position.

A controller of a driving force control apparatus starts shift-change-timing restrain control to limit operation driving force at a timing when a starting condition including a condition that a shift position has changed when an accelerator pedal is in an operating state becomes satisfied, and performs reverse-timing restrain control to limit the operation driving force when a performing condition including a condition that the accelerator pedal is in the operating state as well as the shift position is in a reverse position is satisfied. When a specific operation is performed during the shift-change-timing restrain control being performed, the controller moderates a degree of the limitation to the operation driving force in the shift-change-timing restrain control or stops this control. When the specific operation is performed during the reverse-timing restrain control being performed, the controller maintains a degree of the limitation to the operation driving force in the reverse-timing restrain control.