A hybrid vehicle includes an electric motor and a combustion engine. A K0 clutch couples the combustion engine to a drivetrain of the vehicle. A control module of the vehicle calculates a torque to be applied by the motor to the K0 clutch when initiating engagement of the combustion engine to the drivetrain. The control module calculates two separate torque lead values by two separate methods and calculates the torque by combining the two torque lead values.

A memory device includes a first memory area including a first memory cell array having a plurality of first memory cells each for storing N-bit data according to an M-bit data access scheme, where N is a natural number, and a first peripheral circuit for controlling the first memory cells and disposed below the first memory cell array, a second memory area including a second memory cell array having a plurality of second memory cells each for storing M-bit data according to an M-bit data access scheme, where M is a natural number greater than N, and a second peripheral circuit for controlling the second memory cells and disposed below the second memory cell array, the first memory area and the second memory area are included in a single semiconductor chip and share an input and output interface, and a controller configured to generate calculation data by applying a weight stored in the first memory area to sensing data in response to receiving the sensing data obtained by an external sensor, and store the calculation data in one of the first memory area or the second memory area according to the weight.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a prime mover (8) and of at least one vehicle component influencing energy efficiency of a motor vehicle. The MPC algorithm (13) includes a longitudinal dynamic model (14) of the drive train (7) and of the vehicle component influencing the energy efficiency of the motor vehicle (1) as well as a cost function (15) to be minimized. The cost function (15) includes at least one first term. The processor unit (3) is configured for determining a particular input variable for the prime mover (8) and for the at least one vehicle component influencing the energy efficiency of the motor vehicle (1) by executing the MPC algorithm (13) as a function of a particular term such that the cost function (15) is minimized.

The invention relates to a method for operating a self-propelled motor vehicle having a plurality of control units and a plurality of program codes for controlling the function of autonomous driving and possibly other functions of the self-propelled vehicle, wherein a plurality of program codes used for an autonomous driving mode are redundantly applied to at least two different control units. In doing so, the self-propelled motor vehicle is operated in an at least partially autonomous driving mode. In this mode, the functions directly needed to satisfy the passenger's wishes are ascertained and weighted corresponding to their relevance for satisfying the passenger's wishes. In so doing, the functions, or the scope of functions, are released depending on the achievement of a target achievement level.

A system for estimating tire parameters for an off-road vehicle in real time, the system including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to measure a position of the vehicle at a first time, determine, based on the position, motion characteristics of the vehicle, predict, based on the motion characteristics, a position of the vehicle at a second time, measure a position of the vehicle at the second time, and generate a tire parameter associated with the vehicle based on the predicted position and the measured position of the vehicle at the second time.

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 processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized.

An apparatus, including: an interface configured to receive raw images of one or more objects across a timeseries of frames corresponding to a movement event from a perspective of a vehicle of interest (Vol); and processing circuitry that is configured to: track a change in intensity or direction information represented in motion vectors (MVs) generated based on the raw images; generate, based on the change in the intensity or direction information, a weight of an influence vector representing a Vol influence on the movement event; and transmit the weight of the influence vector and an identity of the movement event to an assessment system that is configured to utilize the weight of the influence vector in an assessment of the Vol.

An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

A control method of a vehicle (1) according to the present disclosure is a control method for controlling the vehicle (1) that has tires (6) mounted thereon and drives autonomously on a course (200) that includes a banked section. The control method includes a setting step of setting a weighting for a plurality of sensors (12) configured to detect information about the vehicle (1) or the course (200), and a detection step of performing detection of a position of the vehicle (1) and/or an obstacle around the vehicle (1) using the weighting for the plurality of sensors (12) and a detection result of the sensors (12). In the setting step, the weighting is changed between the banked section (230) and sections other than the banked section (230).