Provided is a method for controlling movement of an autonomous mobile machine. The method includes that: a target path and a current state of the autonomous mobile machine are acquired; at least one preview distance is calculated according to a current speed; at least one preview point each corresponding to a respective one of the at least one preview distance is acquired according to the target path and the at least one preview distance; a lateral deviation from each preview point to a current position is calculated; a direction control angle parameter of a current control period is acquired according to the lateral deviation, the current speed and a preset parameter matching table; and the autonomous mobile machine is controlled to move according to the direction control angle parameter. Also provided are an apparatus for controlling movement of an autonomous mobile machine, a machine and a storage medium.

A method for operating a two-wheeler. The two-wheeler includes a drive unit and a sensor system, the sensor system including a rotation rate sensor, an acceleration sensor, and a wheel speed sensor. The wheel speed sensor detects at least one measuring pulse per revolution of a wheel of the two-wheeler. The method includes: detecting three-dimensional rotation rates of the two-wheeler, detecting acceleration values of the two-wheeler, and estimating a motion state of the two-wheeler based on the detected rotation rates, the motion state including estimated values for estimated acceleration values and an estimated speed and an estimated distance covered, first correction of the estimated motion state based on the detected acceleration values, ascertaining an instantaneous steering angle of the two-wheeler based on the corrected estimated motion state, and actuating the drive unit and/or an antilocking system of the two-wheeler as a function of the ascertained instantaneous steering angle.

[Solution] A driver abnormality determination system includes circuitry configured to detect a driving operation of a driver, detect behavior of the driver's head and motion of eyeballs and/or movement of a sightline of the driver, recognize a driving scene in which the vehicle is driven; a memory that stores a sensor table of a relationship between the driving scene and detected values to be used to determine a driver abnormality; and determine the driver abnormality based on the sensor table, the driving scene, the driving operation and the behavior.

A hybrid electric vehicle including: (a) an engagement device disposed between an engine and an electric motor; (b) a transmission disposed between the electric motor and drive wheels; (c) an electric storage device configured to supply an electric power to the electric motor; and (d) a control apparatus. When the engine is to be started, the engagement device is engaged to transmit a torque from the electric motor to the engine, for thereby starting the engine. The control apparatus is configured to inhibit stop of the engine, when an outputtable electric power outputtable from the electric storage device is not larger than a threshold value. The threshold value is not smaller than a start-case-required electric power that is required to start the engine, such that a difference value between the threshold value and the start-case-required electric power is not larger than a predetermined value.

A vehicle learning system includes a first execution device mounted on a vehicle, a second execution device outside the vehicle, and a storage device. The storage device stores mapping data including data, which is learned by machine learning and defines mapping that receives input data based on a detection value of an in-vehicle sensor and outputs an output value. The first execution device and the second execution device execute, in cooperation with each other, an acquisition process of acquiring input data, a calculation process of calculating an output value with the input data as an input of the mapping, and a relationship evaluation process of evaluating a relationship between a predetermined variable different from a variable corresponding to the output value and accuracy of the output value. The first execution device executes at least the acquisition process, and the second execution device executes at least the relationship evaluation process.

An autonomous driving control apparatus and method are for determining following-route deviation of an autonomous vehicle. The apparatus and method: may obtain surrounding information of an autonomous vehicle; may calculate a control-following route according to a predetermined driving strategy based on the surrounding information and the high definition map information around an autonomous vehicle; may calculate an expected driving route on which the autonomous vehicle is expected to be driven, when autonomous driving according to the control-following route is performed; may determine whether following-route deviation of the autonomous vehicle is expected, by comparing the control-following route with the expected driving route; and may change the driving strategy based on whether the following-route deviation of the autonomous vehicle is expected.

The disclosure relates to the field of vehicle technologies, and specifically, to a vehicle control method and system, a vehicle, a control apparatus, a vehicle-mounted device, and a computer-readable storage medium. The disclosure aims to solve the following technical problem: Since a distinction between understeering and oversteering conditions is not taken into consideration when increasing the engine torque, there is still room for improvement in a formulated engine torque increasing strategy. For this purpose, the disclosure provides a vehicle control method and system, a vehicle, a control apparatus, a vehicle-mounted device, and a computer-readable storage medium, where the control method includes: when an abnormal state occurs in a vehicle in a steering condition, determining whether the current abnormal state is understeering or oversteering; and adjusting torque of the vehicle based on a determining result and a torque amount adjustment mechanism predetermined for the current abnormal state. Through such settings, a feasible torque adjustment strategy can be provided for each of the understeering and oversteering conditions.

Embodiments of the present invention provide a vehicle having different operating modes, and for each such mode a different characteristic of output torque and accelerator pedal position. The rise of output torque in response to a propulsion request is more or less delayed according to the instant operating mode. The invention provides for blending of the response to a propulsion request so that the delay is progressively varied between a source and target operating mode.

A powertrain assembly has multiple torque actuators. The assembly includes a first controller configured to control a first torque actuator and a second controller configured to control a second torque actuator. The first controller is configured to receive a signal from an input sensor and convert the signal into a torque demand. The second controller is configured to receive the torque demand from the first controller and determine respective optimal torque allocations for the first and second torque actuators based on the torque demand and a plurality of optimization factors. The first controller includes a processor and tangible, non-transitory memory on which is recorded instructions for executing a method of controlling the multiple torque actuators across the at least two controllers via a dynamic look-up table. The dynamic look-up table is populated by a plurality of stored torque production allocation values based on a respective plurality of torque requests.

Techniques are described herein for generating trajectories for autonomous vehicles using velocity-based steering limits. A planning component of an autonomous vehicle can receive steering limits determined based on safety requirements and/or kinematic models of the vehicle. Discontinuous and discrete steering limit values may be converted into a continuous steering limit function for use during on-vehicle trajectory generation and/or optimization operations. When the vehicle is traversing a driving environment, the planning component may use steering limit functions to determine a set of situation-specific steering limits associated with the particular vehicle state and/or driving conditions. The planning component may execute loss functions, including steering angle and/or steering rate costs, to determine a vehicle trajectory based on the steering limits applicable to the current vehicle state.