A method for detecting and processing the carriageway condition of a carriageway on which a vehicle is driven, by means of at least one noise sensor provided on the vehicle, in particular by means of at least one mechanical vibration sensor, wherein noise signals travelling through the vehicle are sensed by a noise sensor and conclusions as to the carriageway condition are drawn from the sensed noise signals. According to said method, the section of route on which the vehicle is currently being driven is determined, the determined carriageway condition is assigned to the section of route, said section of route and the carriageway condition that has been determined and assigned to the section of route are transmitted to a computer network, in particular to a cloud-based computing service, and the information relating to the carriageway condition assigned to a section of route is made available via the computer network.

A vehicle control device performs driving mode switching processing from an automated driving mode to a manual driving mode according to the intention of a vehicle occupant of a vehicle. A vehicle control device includes: a switching controller that is configured to switch a driving mode of a vehicle from an automated driving mode to a manual driving mode based on a predetermined manipulated variable of a steering wheel; and a steering reaction force setting unit that is configured to set, according to how a vehicle occupant grips the steering wheel, a steering reaction force applied when the vehicle occupant steers the steering wheel in the automated driving mode.

A vehicle control device performs driving mode switching processing from an automated driving mode to a manual driving mode according to the intention of a vehicle occupant of a vehicle. A vehicle control device includes: a switching controller that is configured to switch a driving mode of a vehicle from an automated driving mode to a manual driving mode based on a predetermined manipulated variable of a steering wheel; and a steering reaction force setting unit that is configured to set, according to how a vehicle occupant grips the steering wheel, a steering reaction force applied when the vehicle occupant steers the steering wheel in the automated driving mode.

A tire (100) rolls on a surface (105). A method (600) for providing maximum traction coefficient between the tire (100) and the surface (105) include steps for detecting a momentary slip of the tire (100) on the surface (105); detecting a momentary traction coefficient; forming a tuple (410, 510) from the slip and the current traction coefficient; choosing a characteristic curve (205, 305) from a number of predetermined characteristic curves (205, 305) on the basis of the tuple (410, 510), whereby each characteristic curve (205, 305) describes a traction behavior of the tire (100) or a corresponding characteristic pitch; determining the maximum traction coefficient on the basis of the selected characteristic curves (205, 305); and thus providing the maximum traction coefficient.

A tire (100) rolls on a surface (105). A method (600) for providing maximum traction coefficient between the tire (100) and the surface (105) include steps for detecting a momentary slip of the tire (100) on the surface (105); detecting a momentary traction coefficient; forming a tuple (410, 510) from the slip and the current traction coefficient; choosing a characteristic curve (205, 305) from a number of predetermined characteristic curves (205, 305) on the basis of the tuple (410, 510), whereby each characteristic curve (205, 305) describes a traction behavior of the tire (100) or a corresponding characteristic pitch; determining the maximum traction coefficient on the basis of the selected characteristic curves (205, 305); and thus providing the maximum traction coefficient.

A computer system operates to determine a traction value for each of a plurality of regions of a road network. The computer system identifies a region of the road network for which the traction value is known. The computer system may direct a vehicle to operate over a region of the road network where the traction value is known, in order to obtain sensor data that is indicative of a traction capability of the vehicle.

Techniques are described for determining whether snow chains are permitted to be used and to deploy snow chains based on traffic signs, weather conditions, and/or road conditions. An example method of autonomous driving operation includes performing a first determination, by a computer located in an autonomous vehicle, whether a use of snow chains is permitted based at least on a location where the autonomous vehicle is operating, performing a second determination that snow chains are required for driving the autonomous vehicle on a road, and sending, in response to the first determination indicating that the use of snow chains is permitted and in response to the second determination, instruction to snow chain devices located on the autonomous vehicle, where the instruction triggers the snow chain devices to deploy snow chains on tires of the autonomous vehicle.

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 repeatedly selecting control settings for the environment based on (i) a causal model that identifies causal relationships between possible settings for controllable elements in the environment and environment responses that reflect a performance of the control system in controlling the environment and (ii) current values of a set of internal parameters; and during the repeatedly selecting: monitoring environment responses to the selected control settings; determining, based on the environment responses, an indication that one or more properties of the environment have changed; and in response, modifying the current values of one or more of the internal parameters.

An approach is provided for detecting a slippery road condition based on a friction measurement. The approach, for example, involves receiving a traction loss of a vehicle traveling on the road link. The traction loss is detected using a first sensor. The approach also involves receiving a coefficient of friction between the vehicle and a road surface of the road link. The coefficient of friction is measured using a second sensor. The approach further involves fusing the traction loss with the coefficient of friction to detect the slippery road condition on the road link. The approach further involves providing the detected slippery road condition as an output.

A method for off road driving, the method may include obtaining environment sensed information about an environment of a vehicle of a certain model, by one of more vehicle sensors of the vehicle and while driving over an off road path; detecting, by a machine learning process, an off road driving event; determining, by the machine learning process, a characteristic behavior of vehicles of the certain model when facing the off road driving event; and responding, at least in part by the machine learning process, to the occurrence of the off road driving event.