A method for estimating a tire property of a vehicle based on tire-to-road friction properties for a fleet of vehicles. The method includes: determining a tire-to-road friction for a plurality of vehicles, belonging to the fleet of vehicles, at a plurality of specified locations; determining a reference tire-to-road friction for the fleet of vehicles at each specified location; in a vehicle, determining a current tire-to-road friction at a first location being one of the specified locations; determining a difference between the current tire-to-road friction and the reference tire-to-road friction of the fleet for the first location; and estimating a tire property of the vehicle based on the determined difference. There is also provided a system configured to perform the described method.

The present invention relates to a controller (27) for a braking system for a vehicle (10). The braking system has an independent generator (20, 22) on respective front and rear axles (16, 18). The controller (27) comprises an input (44) arranged to monitor a vehicle condition and an operating condition of the generators (20, 22). The controller (27) also comprises a processing means (46) arranged to determine a brake force distribution range between the front and rear axles (16, 18) based on the vehicle condition, and in response to a braking demand and the operating condition of the generators (20, 22), calculate a brake force distribution within the brake force distribution range. In addition, the controller (27) comprises an output (50) arranged to control the generators in accordance with the calculated brake force distribution.

An information processing device includes a processor to execute a program; and a memory to store the program which, when executed by the processor, performs processes of, calculating braking time of a host vehicle; detecting reaction time of the driver of the host vehicle; specifying longer prediction time as the sum of the braking time and the reaction time becomes longer, the prediction time being a range of a time at which a collision between the host vehicle and a surrounding vehicle is predicted in the future; making a prediction of the position and speed of the host vehicle and the position and speed of the surrounding vehicle at a time point included in the prediction time; and predicting, from a result of the prediction, whether or not the host vehicle and surrounding vehicle will collide.

A system may include a processor installed in an aircraft. The processor may be configured to: obtain runway friction coefficient data and runway surface condition data for a runway; obtain braking coefficient data and braking action index data; obtain equivalent runway condition data and runway length data for the runway; obtain aircraft speed data of the aircraft and aircraft configuration data; based at least on the runway friction coefficient data, the runway surface condition data, the braking coefficient data, the braking action index data, the equivalent runway condition data, the aircraft speed data, and the aircraft configuration data, determine a rejected takeoff (RTO) initiating point (RIP) and a start automated RTO sequence point; and cause an automated RTO sequence to be performed if the start automated RTO sequence point is reached without the automated RTO sequence being manually overridden.

The invention relates to a control system for controlling a torque generator of a vehicle. The control system is configured to receive one or more electrical signals indicative of a surface indicator; receive one or more electrical signals indicative of a deceleration demand; select a surface type from a plurality of predetermined surface types based on said one or more electrical signals indicative of a surface indicator; determine a target vehicle deceleration in dependence on the selected surface type; determine, based on said one or more electrical signals indicative of a deceleration demand, a requirement to decelerate the vehicle; and in dependence on determining said requirement, output a control signal to the torque generator. The control signal is configured to cause the torque generator to provide the target vehicle deceleration.

A method in a vehicle for estimating vehicle motion state during a vehicle maneuver, comprising; obtaining a trigger signal indicating an onset of the vehicle maneuver, selecting a sub-set of wheels on the vehicle to be in a free-rolling condition, measuring one or more parameters related to revolution of the sub-set of wheels in free-rolling condition, and estimating the vehicle motion state based on the measured parameters.

An estimation device for estimating a friction coefficient of a road surface includes a sensor, a moment calculator, and a friction coefficient estimator. The sensor is configured to detect a force acting on a wheel. The moment calculator is configured to calculate a moment around a vertical axis at a center of a ground contact load of a tire of the wheel based on an output of the sensor. The friction coefficient estimator is configured to estimate the friction coefficient of the road surface with which the tire is in contact, based on the moment.

A driving assistance apparatus includes a processor having hardware. The processor is configured to acquire vehicle speed data before an ABS of a vehicle is activated and vehicle speed data when the ABS of the vehicle is stopped, calculate a coefficient of sliding friction based on the vehicle speed data before the ABS is activated and the vehicle speed data when the ABS is stopped, determine whether the coefficient of sliding friction is equal to or smaller than a threshold, and detect that a slip due to road freezing has occurred when the coefficient of sliding friction is equal to or smaller than the threshold.

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.