A method to control a road vehicle for the execution of a standing start; the control method comprises the steps of: engaging a gear in a transmission while a corresponding clutch is open; progressively closing the clutch causing the clutch to transmit a torque that causes the rotation of at least a pair of drive wheels; determining a target slip of the drive wheels; cyclically determining a real slip of the of the drive wheels; and continuously modulating the torque transmitted by the clutch during the closing of the clutch based of a difference between the target slip of the drive wheels and the real slip of the of the drive wheels.

In the present invention, when a road surface condition is determined based on information acquired by a camera installed in a vehicle, a route of a host vehicle is predicted and the is determined. A road surface condition determination method and device determines a road surface condition of a predicted route based on information acquired by a camera installed in a host vehicle. A controller predicts a route of the host vehicle by determining a road surface friction coefficient of the predicted route based on information acquired by the camera. The determining of the road surface friction coefficient of the predicted route includes: dividing an ahead-of-vehicle image acquired by the camera in a left-right direction and determining a road surface condition for each of the determination areas, and determining the road surface friction coefficient in the determination areas through which the predicted route will pass.

An information processing system includes one or more vehicles, and a server communicable with the one or more vehicles. The vehicle acquires an image obtained by imaging a road on which a host vehicle is located. The vehicle or the server determines a degree of difficulty in traveling on the road due to snow cover from the image. The server stores the degree of difficulty in traveling for each of one or more roads, and provides information to a client by using the stored degree of difficulty in traveling for each of one or more roads.

An information processing system includes one or more vehicles, and a server communicable with the one or more vehicles. The vehicle acquires an image obtained by imaging a road on which a host vehicle is located. The vehicle or the server determines a degree of difficulty in traveling on the road due to snow cover from the image. The server stores the degree of difficulty in traveling for each of one or more roads, and provides information to a client by using the stored degree of difficulty in traveling for each of one or more roads.

An electronic controller (10) for a motor vehicle (100), the controller being configured to determine when at least one wheel (111, 112, 114, 115) has lost traction, wherein when the controller (10) determines that at least one wheel (111, 112, 114, 115) has lost traction the controller (10) is configured to provide an output to a driver indicative of the at least one wheel (111, 112, 114, 115) that has lost traction.

A method for operating a drive train of a transportation vehicle wherein the drive train is switched between a first operating state, in which a two-wheel drive of the drive train is activated, and a second operating state, in which a four-wheel drive of the drive train is activated. The drive train is switched by an electronic computing device from one of the operating states to the other operating state. During the driving of the transportation vehicle, a demand time is determined by the electronic computing device not later than which the switching from the one operating state to the other operating state must be completed, the demand time lying in the future with respect to the determination of the demand time. The switching from the one operating state to the other operating state is commenced at a starting time in advance of the demand time.

The present disclosure relates to an apparatus (100) for estimating a road condition. The apparatus (100) comprises an input interface (110) configured to receive input data (112) derived from one or more sensors, wherein each sensor is configured to measure a physical quantity related to a vehicle (200) or its environment, wherein the input data (112) features a current driving status of the vehicle, a machine learning processor (120) configured to map the input data (112) to an estimated road condition (122), and a validation processor (130) configured to validate the estimated road condition (122) based on a measurement (132) of the road condition obtained at the current driving status of the vehicle. Validation information from the validation processor (130) can be transmitted to a cloud server (170) for further processing and producing validation swarm knowledge for a whole vehicle fleet.

An autonomous vehicle has a temperature sensor for sensing an air temperature or a road temperature, a processor communicatively connected to the temperature sensor to receive a signal from the temperature sensor, to process the signal and to generate an estimated instantaneous coefficient of friction between a tire of the vehicle and a snow-covered roadway, and a camera to detect salt and/or sand on the roadway and to apply a salt correction factor and/or a sand correction factor to the coefficient of friction to thereby provide a salt-corrected coefficient of friction or a sand-corrected coefficient of friction.

Technologies and techniques for operating an assistance system in a motor vehicle with which a driving a shoulder by the motor vehicle is detected by means of at least one detection device in the assistance system, and with which a control signal is generated by means of an electronic computing device in the assistance system for engaging with a functional unit in the motor vehicle. The shoulder that is detected is evaluated in terms of the type of shoulder, and a critical or non-critical driving of the vehicle on the shoulder is determined on the basis of the type of shoulder, and the control signal is generated in the case of a critical driving on the shoulder. The present disclosure also relates to an assistance system.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.