Provided are a driving force control method and device for a hybrid vehicle, each capable of effectively absorbing torque fluctuation of an engine while suppressing deterioration in energy efficiency. The driving force control device for a hybrid vehicle comprises a PCM configured to: estimate an average torque output by an engine; estimate a torque fluctuation component of the torque output by the engine; set a countertorque for suppressing the estimated torque fluctuation component; and control an electric motor to output the set countertorque, wherein the PCM is operable, under the condition that the average torque output by the internal combustion engine is constant, to set a negative control gain such that, as an engine speed becomes higher, the absolute value of the control gain becomes smaller, and then to set the countertorque based on a product of the estimated torque fluctuation component and the control gain.

In one embodiment, a steering control delay is measured, where the steering delay represents the delay between the time of issuing a steering control command and the time of a response from one or more wheels of an autonomous vehicle. A speed control delay is measured between the time of issuing a speed control command and the time of a response from one or more wheels of the autonomous vehicle or the time of supplying pressure to the gas pedal or brake pedal. In response to a given route subsequently, an overall system delay is determined based on the steering control delay and the speed control delay using a predetermined algorithm. Planning and control data is generated in view of the system delay for operating the autonomous vehicle.

Methods and systems are provided for cruise control velocity tracking. In one example, the method or system may generate a torque command output via a velocity controller that allows for an error within bounds to reduce a fuel consumption amount, the torque command output selected from outcomes of a leader and follower game.

Techniques for using a set of variables to estimate a vehicle velocity of a vehicle are discussed herein. A system may determine an estimated velocity of the vehicle using a minimization based on an initial estimated velocity, steering angle data and wheel speed data. The system may then control an operation of the vehicle based at least in part on the estimated velocity.

Described herein is a method and arrangement of curve speed adjustment for a road vehicle (1). Obtained is data on: current ego velocity (v.sub.E), distance (d) and curvature (r) of an upcoming road segment, represented by a set of control points (P.sub.n, P.sub.n+1, etc.) to be negotiated; road property of a road comprising the road segment; environmental properties; and driver properties. The obtained data is continuously streamed to a data processing arrangement (12) arranged to perform a translation to target velocities (v.sub.road, n, v.sub.road, n+1, etc.) for the respective control points (P.sub.n, P.sub.n+1, etc.) and, for each respective control point (P.sub.n, P.sub.n+1, etc.), a translation from target velocity (v.sub.road, n, v.sub.road, n+1, etc.) for that control point (P.sub.n, P.sub.n+1, etc.) and distance (d.sub.n, d.sub.n+1, etc.) to that control point (P.sub.n, P.sub.n+1, etc.) and obtained current ego velocity (v.sub.E), to a target acceleration (a.sub.n, a.sub.n+1, etc.) to reach that control point (P.sub.n, P.sub.n+1, etc.) at its target velocity (v.sub.road, n, v.sub.road, n+1, etc.). The resulting target accelerations (a.sub.n, a.sub.n+1, etc.) are continuously streamed to a control unit (14) of the road vehicle (1) to adjust the road vehicle (1) acceleration to reach each respective control point (P.sub.n, P.sub.n+1, etc.) at its target velocity (v.sub.road, n, v.sub.road, n+1, etc.).

A hybrid powertrain system includes an engine and an electric machine respectively connected to first and second drive axles, with the electric machine decoupled from the engine. The system includes a battery pack and a controller. The controller has slip integrators with a corresponding integrator value for a given one of the drive axles. The integrator values are indicative of an accumulated amount of drive wheel slip over a calibrated duration or window. The integrator values change responsive to axle torque and traction control status signal. The integrator values are added together to derive an integrator sum. Responsive to the integrator sum exceeding a calibrated integrator threshold, the controller executes a control action, including automatically executing a Weather Mode in which energy use of the battery pack is reserved for traction control/propulsion of the vehicle.

The present technology relates to a vehicle control apparatus, a vehicle control method, and a movable object that make it possible to realize travelling in a more appropriate operation mode.

An acquisition unit acquires, as information regarding an operation mode of a different vehicle, operation mode information indicating the operation mode of the different vehicle or operation mode switching information indicating that the different vehicle has switched the operation mode, for example. A determination unit determines an operation mode of a host vehicle in accordance with the received information regarding the operation mode of the different vehicle (e.g., the operation mode information indicating the operation mode of the different vehicle or the operation mode switching information indicating that the different vehicle has switched the operation mode). The present technology is applicable to, for example, an ECU for controlling a vehicle that performs automatic driving.

A method for generating a vehicle human machine interface is disclosed. The identities of each of a plurality of occupants in a vehicle are determined. A plurality of interface settings corresponding to the plurality of occupants are obtained according to the identities. A machine learning operation is performed according to the identities of the plurality of occupants and the plurality of interface settings. The vehicle human machine interface is generated according to a result of the machine learning operation.

The present disclosure relates to a method for state estimation of a target comprising: obtaining an observation variable of the target at different moments through a plurality of sensors, wherein at least one observation variable is acquired by each sensor; determining a state variable of the target at different moments based on the observation variable; and optimizing the state variable of the target by minimizing a loss function. The loss function includes at least one of a position loss, an orientation loss, a velocity loss, a size loss, or a structural constraint of the target. The method of the present disclosure may obtain a sufficiently accurate state estimate. In addition, an apparatus, an electronic device, and a medium for state estimation of the target are also provided.

Provided is a vehicle control device capable of improving robustness against an attitude change of an occupant during preceding vehicle follow-up control in a straddle type vehicle such as a motorcycle. A vehicle control device 100 for a straddle type vehicle that controls a driving force during preceding vehicle follow-up control for causing an own vehicle to follow a preceding vehicle, in which the driving force (which is a control amount of the preceding vehicle follow-up control) is corrected according to an attitude (attitude change) of a driver detected by an attitude detection device 800 during the preceding vehicle follow-up control. Further, the driving force is corrected on the basis of the attitude, the driving torque of the own vehicle, and the acceleration of the own vehicle. In addition, the driving force (according to the attitude) is corrected from at least one of the own vehicle speed of the own vehicle, the target speed, the inter-vehicle distance between the own vehicle and the preceding vehicle, the target inter-vehicle distance, and the relative speed.