An electronic system for controlling vibrations and/or inertial forces occurring at a plurality of areas of interest within an operating vehicle, the electronic device comprising circuitry configured to: receive input data comprising sensor data from one or more environment sensors (12) and/or one or more internal sensors (14); convert, by means of a machine learning system (18), the input data into actuator settings; and transmit the actuator settings to one or more actuators (20) to control vibrations and/or inertial forces occurring at each of the plurality of areas of interest within 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.

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 controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.

A system for parallel parking a vehicle in a target parking space is provided herein. A sensing system is configured to detect objects located proximate the target parking space. A park assist system is communicatively coupled to the sensing system. The park assist system is configured to automatically steer the vehicle during a backing maneuver into the target parking space while limiting the speed of the vehicle based on a steering wheel angle. The sensing system is also configured to automatically steer the vehicle during maneuvers inside the target parking space while limiting the speed of the vehicle based on a relative position of at least one of a rear bounding object and a front bounding object.

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 electronic system for controlling vibrations and/or inertial forces occurring at a plurality of areas of interest within an operating vehicle. The electronic device comprises circuitry configured to receive input data comprising sensor data from one or more environment sensors and/or one or more internal sensors; convert, by means of a machine learning system, the input data into actuator settings; and transmit the actuator settings to one or more actuators to control vibrations and/or inertial forces occurring at each of the plurality of areas of interest within the vehicle.

Systems, devices, integrated circuits, and methods are directed to on-vehicle data processing using analog hardware realization of neural networks. A vehicle obtains a temporal sequence of sensor data that is collected by a microphone of a sensor system. The sensor system is physically coupled to a tire of a vehicle. The neural network circuit generates one or more output data items based on the sensor data, and the one or more output data items indicate the condition of a road, the vehicle, or a component of the vehicle. The sensor system, including an electronic device that includes the microphone, is also described herein. A method of training the neural network is also described herein.

An acoustic sensor is configured to be attached to a cover member covering an outer circumferential portion of a tire of a vehicle and measure information related to sound vibrations. The acoustic sensor includes a sensor support, a facing surface, and a vibration detector. The sensor support is attached to a cover outer surface of the cover member. The cover member has a curved plate portion extending in a circumferential direction of the tire. The cover outer surface is one surface of the curved plate portion that faces away from the tire. The facing surface faces the cover outer surface to receive the sound vibrations of the cover outer surface when the sensor support is attached to the cover outer surface. The vibration detector is configured to detect the sound vibrations that are received by the facing surface.

A device and operating method are provided for an interaction between a vehicle, which can travel in an at least partially automated manner, with an operator control element, which can be operated by the user of the vehicle for influencing at least the transverse guidance of the vehicle. A wheel positioning angle and/or a drive torque or braking torque at at least one wheel of the vehicle can be controlled in response to an operator control action at the operator control element or as a function of a control unit for controlling at least partially automated travel. Depending on the degree of haptic contact between the user of the vehicle and the operator control element, at least one movement of the vehicle can be executed on the basis of one or more operator control actions of the user and/or on the basis of data of the control unit for controlling the at least partially automated travel.