A system for a vehicle includes a first input device and a second input device that provides a plurality of selectable input options. The plurality of selectable input options comprises a plurality of control input options for controlling a plurality of vehicle functions and an association input option for associating the first input device with at least one vehicle function suggested from among the plurality of vehicle functions. The at least one vehicle function is suggested based on historical use of the control input option that controls the at least one vehicle function and an accessibility score of the control input option.

An auto parking system of a vehicle is disclosed. The auto parking system includes a planner initialization module configured to perform sampling of both a start state representing a state of the vehicle and an end state representing a state of the parking target; a path planning module configured to perform tree iteration of a start tree from the start state and an end tree from the end state and connect nodes from the start tree and the end tree to generate path data; and a trajectory planning module configured to receive the path data from the path planning module and construct a trajectory of the vehicle based on the path data; where the start tree and end tree comprise two different types of tree structures.

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 device for identifying a road surface includes: storage for storing a deep learning-based road surface model; and a controller configured to identify a type of a road surface on which a vehicle is currently traveling, using the road surface model. The device for identifying a road surface can identify a type of a road surface on which the vehicle is traveling based on deep learning and control the terrain mode of the vehicle based on the identified type of the road surface. The type of the road surface on which the vehicle is traveling may be identified with a high accuracy and an optimal terrain mode may be set, thereby improving not only travel stability but also riding comfort of the vehicle.

A method of adaptive trajectory generation for a vehicle is provided. A computer device of the vehicle may update a current trajectory for the vehicle when some predetermined conditions that are related to an obstacle positioned within a predetermined distance of the vehicle are satisfied.

Disclosed is a method for controlling a hybrid vehicle power train, including a thermal drive chain and an electric drive chain, the electric drive chain including a traction battery, a voltage modulator, an inverter, first and second electrical machines. The voltage modulator is designed to modulate a supply voltage of an electric current from the traction battery to the first and second electrical machines. The method includes: a step of analytically calculating an optimal supply voltage using a mathematical expression that corresponds to the resolution of an equation expressed as

[00001]$\frac{\partial P_{bat}}{\partial U_e}=0,$

where U.sub.e is the supply voltage, P.sub.bat is the electrical power supplied by the traction battery, and where the electrical power supplied by the traction battery is expressed as a quadratic function of the supply voltage; and a step of controlling the voltage modulator in such a way that it outputs the optimal supply voltage.

A system can train a vehicle electronically to accommodate a driver. The system can train the vehicle to accommodate the ability, condition, and/or personality of the driver. The system can change the controls of the vehicle, responsive to the inputs from the driver, to match with the patterns of controls resulting from a predetermined model (such as a safe-driver model). Accordingly, the vehicle can appear as it is being driven by a safe driver when it may not be the case. A driver with a lower driving competence may apply physical controls in a pattern that may be slow, unstable, or insufficient. However, the vehicle can be trained to adjust the transformation from the UI signals to the drive-by-wire signals such that the transformed signals appear to be applied by a more competent driver on the road. And, the transformation can improve over time with training via machine learning.

A device for identifying a road surface includes: storage for storing a deep learning-based road surface model; and a controller configured to identify a type of a road surface on which a vehicle is currently traveling, using the road surface model. The device for identifying a road surface can identify a type of a road surface on which the vehicle is traveling based on deep learning and control the terrain mode of the vehicle based on the identified type of the road surface. The type of the road surface on which the vehicle is traveling may be identified with a high accuracy and an optimal terrain mode may be set, thereby improving not only travel stability but also riding comfort of the vehicle.

Systems and methods of the present disclosure are directed to a method. The method can include obtaining simplified scenario data associated with a simulated scenario. The method can include determining, using a machine-learned perception-prediction simulation model, a simulated perception-prediction output based at least in part on the simplified scenario data. The method can include evaluating a loss function comprising a perception loss term and a prediction loss term. The method can include adjusting one or more parameters of the machine-learned perception-prediction simulation model based at least in part on the loss function.

A method of adaptive trajectory generation for a vehicle is provided. A computer device of the vehicle may update a current trajectory for the vehicle when some predetermined conditions that are related to an obstacle positioned within a predetermined distance of the vehicle are satisfied.