The present techniques generally relate to a computer implemented method of initiating autonomous drive of a vehicle when the drive of the vehicle is under the control of a user, the method comprising: detecting or predicting the start of a user sneezing episode; and initiating autonomous drive of the vehicle during the user sneezing episode. The method may additionally involve, after the initiating the autonomous drive of the vehicle, determining the end of the user sneezing episode, ending the autonomous drive of the vehicle and reverting the drive of the vehicle back to the control of the user. All of this may be done without the user of the vehicle being aware of the autonomous drive of the vehicle.

A system and method for computationally estimating a tire normal force for use in vehicle antilock braking, adaptive cruise control, and traction and stability control by correcting measured accelerations with respect to the estimated road angles. The system and method are operative to measure an acceleration at three points on a sprung mass of the vehicle and estimate a tire normal force of a tire in response to the three acceleration measurements as an input to the vehicle controller.

A device for estimating a friction coefficient between a road surface and an automotive tire through determination of a steering torque during a steering maneuver of a slow-rolling or stationary vehicle includes a computer configured for constructing a brush model for a description of the steering torque across a contact patch between the tire and road surface. The steering torque is a torque acting on a steering axis required to overcome resistance to tire twisting on the road surface at a wheel velocity and a steering rate. The steering torque depends on a tire brush vertical load distribution and relative motion of tire brushes and the road surface. The device further includes sensors for measuring the wheel velocity and the steering rate and mechanism for measurements or estimation of the steering torque. The friction coefficient is estimated based on the measurements or estimation of the steering torque and the brush model.

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

A vehicle control device including a tire-side device and a vehicle-side device is provided. The tire-side device includes a vibration detection unit that outputs a detection signal corresponding to a magnitude of vibration of a tire, a signal processing unit that generates data representing a friction coefficient between the tire and a road surface by processing the detection signal, and a transmitter that transmits the data. The vehicle-side device includes a receiver that receives the data and a travel control unit that estimates the friction coefficient based on the data, acquires a braking distance of the vehicle based on the friction coefficient, and controls acceleration and deceleration of the vehicle based on the braking distance.

An autonomous vehicle configured for active sensing may also be configured to weigh expected information gains from active-sensing actions against risk costs associated with the active-sensing actions. An example method involves: (a) receiving information from one or more sensors of an autonomous vehicle, (b) determining a risk-cost framework that indicates risk costs across a range of degrees to which an active-sensing action can be performed, wherein the active-sensing action comprises an action that is performable by the autonomous vehicle to potentially improve the information upon which at least one of the control processes for the autonomous vehicle is based, (c) determining an information-improvement expectation framework across the range of degrees to which the active-sensing action can be performed, and (d) applying the risk-cost framework and the information-improvement expectation framework to determine a degree to which the active-sensing action should be performed.

A detection device includes: a detection circuit that performs detection processing based on a signal from a physical quantity transducer and outputs detection data; an interface that has communication connection with an external device and outputs the detection data to the external device; and a processing circuit. The processing circuit outputs the detection data acquired from the detection circuit at a common acquisition timing common to at least one other detection device and the own detection device, to the interface in a data transmitting order of own detection device.

A vehicle including a monitoring system and controller for evaluating a vehicle subsystem is described. The monitoring system includes a sensor that is disposed to monitor on-vehicle noise or vibration. The subsystem includes an actuator, and a fault associated with the subsystem is defined by a fault vibration signature. A command to activate the subsystem is monitored coincident with a signal input from the sensor. A first vibration signature is determined based upon the signal input from the sensor, and a correlation between the first vibration signature and the fault vibration signature are determined for the fault associated with the subsystem. Occurrence of a fault associated with the subsystem can be detected when the correlation between the first vibration signature and the fault vibration signature associated with the subsystem is greater than a threshold correlation.

A method and a device are provided for deactivating a driver assistance system for autonomous transverse guidance of a vehicle, which driver assistance system has a sensor unit for detecting a contact with the rim of a steering wheel by a driver, and a control unit which detects, via the detected contact, a request by the driver to transfer from the autonomous transverse guidance into a manual transverse guidance by the driver. The pressure sensor constitutes at least one pressure sensor, which extends over at least that part of the surface of the steering wheel rim which is usually touched by the driver's hands. It does not extend over the area of the surface of the steering wheel rim which, viewed in a cross section of the steering wheel rim, describes an approximately circular-segment-shaped arc which extends over a sector angle of the order of magnitude of 90?, which arc is spaced apart as far as possible from the center point of the steering wheel.

The invention relates to a sensor arrangement (3) for detecting a state of a road (11), with a sensor device (9) which is designed to detect an impact of water (12) on a wheel arch lining (13) of a motor vehicle (1) while the motor vehicle (1) is travelling on the road (11), and with a control device (7) for detecting the state of the road (11) on the basis of the impact of the water (12) detected by means of the sensor device (9), wherein the sensor device (9) has a first and a second ultrasound sensor (4, 5) which are designed in each case to receive an ultrasound signal and which are furthermore designed in each case to detect the impact of the water (12) on the wheel arch lining (13), wherein the first and the second ultrasound sensor (4, 5) are arranged apart from one another on or in the wheel arch lining (13).