A magnetic torque sensing device having a torque transferring member with a magnetoelastically active region. The magnetoelastically active region has oppositely polarized magnetically conditioned regions with initial directions of magnetization that are perpendicular to the sensitive directions of magnetic field sensor pairs placed proximate to the magnetically active region. Magnetic field sensors are specially positioned in relation to the torque-transferring member to accurately measure torque while providing improved RSU performance and reducing the detrimental effects of compassing. The torque sensing devices are incorporated on vehicle drive train components, including differential components, transfer case components, transmission components, and others, including on power transmission shafts, half-shafts, and wheels, and output signals representing characteristics of the vehicle are processed in algorithms to provide useful output information for controlling actions of the vehicle.

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 system (1) switches control between a restart period after restart of a function of controlling traveling of the vehicle following a parking period during which the function is stopped, until the vehicle moves and first detects a magnetic marker and a normal travel period after the vehicle detects the magnetic marker following the restart period. In the restart period, restart control (S105) is performed in which a position of the vehicle is identified based on a position measured in the restart period to cause the vehicle to travel. In the normal travel period, normal travel control (S107) is performed in which the position of the vehicle is identified based on a position of the detected magnetic marker to cause the vehicle to travel.

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.

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 method for determining a side slip angle in a vehicle, includes determining with a first sensor an orientation of an effective vehicle speed vector of the Vehicle in relation to a geographic coordinate system of the earth; determining with a second sensor an orientation of the vehicle in relation to a magnetic coordinate system of the earth magnetic field; determining a differential angle of the magnetic north direction of the earth relative to the geographic north direction of the earth by using a vehicle speed; and determining the side slip angle as a function of the orientation of the effective vehicle speed vector and the differential angle according to the relationship =.sub.course+.sub.mag,.sub.mag, wherein designates the side slip angle, .sub.mag, the differential angle, .sub.mag the orientation of the vehicle in relation to the magnetic north direction and .sub.course the orientation of the vehicle speed vector.

A vehicle control system is applied to a vehicle equipped with a magnetic sensor configured to detect a magnetic marker on a road. The vehicle control system executes a self-driving control by which self-driving of the vehicle is controlled. The vehicle control system executes a retreat traveling control using the magnetic marker in response to the occurrence of an abnormality in at least part of components and functions necessary for the self-driving control. The magnetic marker provides guidance information by which the vehicle is guided to a safe area. In the retreat traveling control using the magnetic marker, the vehicle control system acquires the guidance information from the magnetic marker detected by the magnetic sensor and causes the vehicle to travel toward the safe area and stop at the safe area based on the guidance information thus acquired.

An information presentation method is for presenting information to a user in a vehicle interior of a vehicle for transporting a cargo. The method includes displaying first guidance information indicating, on a road map, at least part of a first route from a current position of the vehicle to a destination of the vehicle. The first guidance information is displayed on a display provided in the vehicle interior. The method includes switching the first guidance information displayed on the display to second guidance information when the vehicle reaches a predetermined range from the destination. The second guidance information indicates, on a premises map of the destination, at least part of a second route to a stop position at which the vehicle is required to stop for handling the cargo on premises of the destination.

A sensor and method for sleep position detection including: a transmitter configured to transmit electromagnetic waves between 30 GHz and 300 GHz; a receiver configured to receive the electromagnetic waves from the transmitter, wherein the transmitter and receiver are positioned in relation to person sleeping such that the receiver receives reflected electromagnetic waves; and a control station configured to analyze the transmitted and received electromagnetic waves to determine a position of the person sleeping. In some cases, the method may include: forming a radar cube of results; performing a fast fourier transform (FFT) on the radar cube; applying a constant false alarm rate (CFAR) processor to the FFT data; determining a capon gradient; forming a 5-dimensional feature space based on the capon gradient; and conducting an optimization of SVM.

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.