Techniques are described to determine parameters and/or values for a control model that can be used to operate an autonomous vehicle, such as an autonomous semi-trailer truck. For example, a method of obtaining a data-driven model for autonomous driving may include obtaining data associated with a first set of variables that characterize movements of an autonomous vehicle over time and commands provided to the autonomous vehicle over time, determining, using at least the first set of data, non-zero values and an associated second set of variables that describe a control model used to perform an autonomous driving operation of the autonomous vehicle, and calculating values for a feedback controller that describes a transfer function used to perform the autonomous driving operation of the autonomous vehicle driven on a road.

Systems, methods, and other embodiments described herein relate to improving controls in a device according to risk. In one embodiment, a method includes, in response to receiving sensor data about a surrounding environment of the device, identifying objects from the sensor data that are present in the surrounding environment. The method includes generating a control sequence for controlling the device according to a risk-sensitivity parameter to navigate toward a destination while considering risk associated with encountering the objects defined by the risk-sensitivity parameter. The method includes controlling the device according to the control sequence.

Testing autonomous vehicle control systems in the real world can be difficult, because creating and re-creating physical scenarios for repeated testing may be impractical. In some implementations, detailed map data and data acquired through driving in a region can be used to identify similar segments of a drivable surface. Simulation scenarios used to test one of the similar segments may be used to test other of the similar segments. The driving data may also be used to generate and/or validate the simulation scenarios, e.g., by re-creating scenarios encountered while driving in a first segment in a simulation scenario for use in the second segment and comparing simulated driving behavior with the driving data.

A method for controlling a vehicle on a mission, the vehicle comprising a first and a second power source for driving the vehicle itself, wherein the first power source comprises an engine configured to generate power from fuel and an after treatment system coupled to the combustion engine, the method comprising the steps of solving a convex first optimal control problem based on a mathematical model of the vehicle, the first optimal control problem involving state variables for the after treatment system, a set of constraints, and a cost function having control variables that include a discrete variable and a continuous variable; the solving including an initial determination of the discrete variable and a iterative execution of minimizing the cost function after replacement of the discrete variable with respect to the continuous variable, updating the discrete variable, and verifying the satisfaction of a convergence criterion.

A computer-based system for testing a server-based vehicle function, which is designed to implement a method comprising the following steps: a function model of the vehicle function is simulated by a first simulator on a server, an at least partial vehicle model is simulated by a second simulator and the vehicle function is tested, while a data connection between the first simulator and the second simulator is systematically influenced.

A method includes identifying a desired path for an ego vehicle. The method also includes determining how to apply steering control and torque vectoring control to cause the ego vehicle to follow the desired path. The determination is based on actuator delays associated with the steering control and the torque vectoring control and one or more limits of the ego vehicle. The method further includes applying at least one of the steering control and the torque vectoring control to create lateral movement of the ego vehicle during travel. Determining how to apply the steering control and the torque vectoring control may include using a state-space model that incorporates first-order time delays associated with the steering control and the torque vectoring control and using a linear quadratic regulator to determine how to control the ego vehicle based on the state-space model and the one or more limits of the ego vehicle.

A system includes a processor configured to receive a user profile responsive to an efficiency determination request for a vehicle model. The processor is also configured to obtain efficiency-affecting data from the user profile. The processor is further configured to compare the efficiency-affecting data to data gathered from drivers of the vehicle model, to determine a correlation between the user profile and similar drivers of the vehicle model. Also, the processor is configured to predict fuel efficiency for the new vehicle model based on efficiency achieved by the similar drivers.

A power distribution control system of a vehicle includes a driving information provider for collecting and providing information required for power distribution control of an engine and a motor in the vehicle; a communication unit for transmitting the information provided by the driving information provider from the vehicle; a cloud server outside the vehicle for selecting and transmitting optimal power distribution control logic data corresponding to a driving situation of the vehicle based on the information provided through the communication unit from the vehicle; and a vehicle controller for performing power distribution control of the engine and the motor based on real-time driving state variable information of the vehicle using the optimal power distribution control logic data received through the communication unit by the vehicle from the cloud server.

A method for determining if a vehicle control command for controlling a vehicle (1) associated with a current vehicle state precludes a future situation avoidance maneuver (SAM) by the vehicle. The method comprises obtaining one or more safe sets, wherein each safe set represents a range of vehicle states from which a future SAM can be initialized with prospect for success. The method also comprises obtaining the current vehicle state and the control command and predicting a future vehicle state based on the current vehicle state and on the control command. The method comprises comparing the predicted future vehicle state to the one or more safe sets and determining that the control command precludes the future SAM if the predicted future vehicle state is not comprised in any of the one or more safe sets.

A system includes an inertial navigation system module (INS module) that detects vehicle yaw rates and vehicle lateral speeds, a controller circuit communicatively coupled with the INS module. The controller circuit determines a tire cornering stiffness (C.sub.f, C.sub.r) based on vehicle physical parameters and vehicle dynamic parameters. The controller circuit determines a vehicle moment of inertia (Iz) based on the vehicle physical parameters, the vehicle dynamic parameters, and the tire cornering stiffness (C.sub.f, C.sub.r).