The present disclosure relates to systems and methods for autonomous driving. The systems may obtain driving information associated with a vehicle; determine a state of the vehicle; determine one or more candidate control signals and one or more evaluation values corresponding to the one or more candidate control signals based on the driving information and the state of the vehicle by using a trained control model; select a target control signal from the one or more candidate control signals based on the one or more evaluation values; and transmit the target control signal to a control component of the vehicle.

Embodiments include methods, systems and computer readable storage medium for a method for collision avoidance by a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides collision avoidance guidance based on training data using movement information from one or more agents and behaviors associated with one or more individuals associated with the one or more agents or the vehicle. The method further includes detecting, by a processor, a collision course between the vehicle and the one or more mobile agents and/or one or more stationary agents. The method further includes calculating, by the processor, one or more decisions that avoid a collision in response to detecting a collision course. The method further includes selecting, by the processor, a decision from the one or more decisions and controlling, by the processor, operation of the vehicle based on the selected decision.

A device for determining a tachometer characteristic curve of a motor vehicle includes a detection apparatus and a determining apparatus. The determining apparatus is configured to form a tachometer characteristic curve on the basis of one or more mean value pairs, which are formed by means of one locomotion signal mean value and one speed mean value, by means of which tachometer characteristic curve locomotion signals and speed values are converted into one another, and/or to change a tachometer characteristic curve which is already present.

The present disclosure provides a method for learning a kisspoint of an engine clutch in a hybrid vehicle, which performs kisspoint learning of an engine clutch while driving by learning hydraulic pressure at the time when motor torque varies by gradually increasing clutch hydraulic pressure in an open state of the engine clutch when a driving load of a vehicle is constant to increase a kisspoint learning frequency of the engine clutch and improve kisspoint accuracy.

A vehicle control apparatus includes an electronic control unit. The electronic control unit is configured to perform feedback control of a motor such that a torque is output for stopping a crankshaft at a target angle. A first angle is used as the target angle during a first period from start of the feedback control to first detection of rotation of the crankshaft in a negative rotational direction for returning the crank angle. A second angle is used as the target angle during a second period from the lapse of the first period to detection of a changeover in a rotational direction of the crankshaft from the negative to a positive rotational direction. The electronic control unit is configured to return the target angle to the first angle at a first timing after the lapse of the second period.

Provided is a vehicle control device with which it is possible to reduce the time required to rewrite a control program. In the vehicle control device according to the present invention, after an updated version of the control program has been stored in a second storage unit, a first storage unit is initialized in advance before the instruction to update the control program has been executed.

A method in which the position of the center of gravity of a moving motor vehicle is ascertained, wherein at least one set of related input variables is taken into consideration, and the set of input variables includes at least a longitudinal acceleration of the motor vehicle, a lateral acceleration of the motor vehicle, a yaw rate of the motor vehicle and at least one wheel rotational speed, in particular four wheel rotational speeds, wherein the set of input variables is ascertained during a steady-state driving maneuver, and a quantity of possible center of gravity positions is defined as classes and, by a learning-based classification method, on the basis of the set of input variables, a class is selected which indicates an estimated center of gravity position. A control unit for carrying out the method is also disclosed.

A state predicting circuitry predicts a route showing a future change in the vehicle state from among a plurality of routes from a first node to a second node. The first node corresponds to the current vehicle state. The second node corresponds to the vehicle state after having transitioned a predetermined number of times from the first node. The state predicting circuitry predicts a route in which at least one of an accumulated value of the node that exists in the routes and an accumulated value of the link that exists in the routes is greatest, from among the plurality of routes.

A vehicle dynamics control system receives a feedback state signal including values of a roll rate and a roll angle of the motion of the vehicle and updates parameters of a model of roll dynamics of the vehicle by fitting the received values into the roll dynamics model. The roll dynamics model explains the evolution of the roll rate and the roll angle based on the parameters including a center of gravity (CoG) parameter modeling a location of a CoG of the vehicle, and a spring constant and a damping coefficient modeling suspension dynamics of the vehicle. The system determines a control command for controlling at least one actuator of the vehicle using a motion model including the updated CoG parameter and submits the control command to the vehicle controller to control the motion of the vehicle.

A controller is provided for operating a system under admissible states. The controller includes an interface configured to connect the system storing a set of measured system states, a set of reference inputs and a set of system parameters in a storage arranged inside or outside the system, a memory storing measured system states, admissible reference inputs and admissible parameter sets and computer-executable programs including a parameter estimator and an adaptive reference governor (ARG), a processor, in connection with the memory. The processor is configured to perform the ARG and the parameter estimator. The parameter estimator extracts a pair of a reference input and the system state and compute a system parameter estimate based on the reference input and system state. The ARG is configured to update the reference input and compute a parameter-robust constraint admissible set based on the updated reference input and the system states, wherein the ARG generates and transmits a reference input to the system based on the parameter-robust constraint admissible set.