Autonomous driving of a vehicle in which computerized perception by the vehicle, including of its environment and of itself (e.g., its egomotion), is used to autonomously drive the vehicle and, additionally, can also be used to provide feedback to enhance performance, safety, and/or other attributes of autonomous driving of the vehicle (e.g., when certain conditions affecting the vehicle are determined to exist by detecting patterns in or otherwise analyzing what is perceived by the vehicle), such as by adjusting autonomous driving of the vehicle, conveying messages regarding the vehicle, and/or performing other actions concerning the vehicle.

Embodiments described herein provide a method for using one or more audio signals from one or more sensors to establish the presence and severity of precipitation at a particular location. Methods may include: receiving at least one first audio signal from a first audio sensor of a vehicle; extracting acoustical features including frequency and amplitude from the at least one first audio signal; receiving at least one second audio signal from a second audio sensor of the vehicle; extracting acoustical features including frequency and amplitude from the at least one second audio signal; processing the frequency and amplitude from the at least one first audio signal and the frequency and amplitude from the at least one second audio signal as inputs to an algorithm to generate an output from the algorithm; and determining, from the output of the algorithm, a precipitation condition and a confidence measure of the precipitation condition.

A vehicle with one-pedal driving mode includes a first axle having a first electric machine configured to power first wheels and a second axle having a second electric machine configured to power second wheels. A controller is programmed to, in response to a request for one-pedal driving mode, map pedal positions of the accelerator pedal to speeds of the first and second wheels such that each of the pedal positions corresponds to a driver-demanded speed of the first and second wheels, and control one or more of the electric machine so that the vehicle is propelled according to the driver-demanded speed.

A vehicle control device controls a driving device in such a manner that driving torque coincides with normal torque. The vehicle control device starts a traction control for controlling the driving device in such a manner that the driving torque coincides with suppressed torque which is smaller than the normal torque, when a predetermined traction control start condition is satisfied. The vehicle control device determines that the driver is in a non-grasp state, when an operation amount has changed to satisfy a condition in an initial determination time period. The vehicle control device starts, at an acceleration time point, an operation priority control for controlling the driving device in such a manner that the driving torque coincides with acceleration priority torque which is larger than the suppressed torque and smaller than the normal torque, if the control device had not determined that the driver was in the non-grasp state.

The present disclosure relates to a method for driving on the basis of characteristics of a driving surface, and a robot for implementing the same, and a method for driving on the basis of characteristics of a driving surface, according to one embodiment of the present disclosure, comprises the steps in which: a sensing module of the robot senses an adjacent driving surface to generate characteristic information of the driving surface, and a control unit of the robot stores position and characteristic information of the driving surface in a map storage of the robot; the controller of the robot sets a function to be applied to the driving surface in response to the characteristic information of the driving surface, or generates a movement path selectively including the driving surface corresponding to start and end points of the robot; and the controller controls a moving unit and a functional unit of the robot according to the set function or the movement path.

An arithmetic model generation system includes a sensor information acquisition unit, a tire force calculator, and an arithmetic model update unit. The sensor information acquisition unit acquires acceleration of a tire. The tire force calculator includes an arithmetic model for calculating tire force F based on the acceleration, and calculates the tire force F by inputting the acceleration acquired by the sensor information acquisition unit. The arithmetic model update unit compares tire axial force measured by the tire and the tire force F calculated by the tire force calculator, and updates the arithmetic model.

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

The present invention relates to the field of vehicles, in particular to a vehicle slip regulation method and apparatus, an electronic device and a medium. The vehicle slip regulation method comprises the following steps: determining a pavement type of a vehicle driving pavement in response to a vehicle acceleration slip regulation event; determining an overall target acceleration of a vehicle according to the determined pavement type, the speed of a non-driving wheel and the slip time of a driving wheel; and performing vehicle slip regulation according to the overall target acceleration. The method does not need to determine wheel adhesion coefficient of a vehicle according to coefficients such as vehicle weight and road slope, can perform slip regulation by calculating overall target accelerations under different pavements, and has strong practicability and robustness and good acceleration slip regulation effect.

A backup control unit for controlling motion of a heavy-duty vehicle during a minimum risk maneuver, where the backup control unit is arranged to receive data indicative of a planned sequence of vehicle control commands from a main vehicle control unit. The backup control unit comprises a first vehicle model configured to map the planned sequence of vehicle control commands into a desired vehicle behavior and is arranged to obtain a measured vehicle behavior from one or more vehicle sensors. Also, the back-up control unit is arranged to determine an adjusted sequence of vehicle control commands based on the planned sequence of vehicle control commands and on a deviation between the desired vehicle behavior and the measured vehicle behavior, and to transmit the adjusted sequence of vehicle control commands to a motion support device, MSD, control unit of the vehicle.

A control apparatus for a vehicle includes an addition portion that outputs a post-compensation driver request torque, which is acquired by adding a driving torque (a loss compensation driving torque) lost due to a braking torque provided to a wheel on a low-μ road surface side according to a BLSD request hydraulic pressure calculated by a BLSD request hydraulic pressure calculation processing portion to a driver request torque calculated by a driver request torque calculation processing portion, to a motor.