Systems and methods are provided for using sensors and signal processing to detect vehicle surface impacts. In particular, a sensor and signal processing approach is provided for detecting impacts, with the results having a low false positive rate. The approach reduces operator costs by reducing operator involvement through improved automated detection technology. Additionally, systems and methods are provided for distinguishing chassis-driven fascia vibration from impact-driven fascia vibration.

A physical quantity sensor includes a driven section and a drive spring that supports the driven section so that the driven section is displaceable in a first direction. The drive spring has a serpentine shape and includes a plurality of spring structures extending in a second direction that intersects a first direction. At least one of the spring structures has a thin section that is thinner in a third direction that intersects the first and second directions than the other portions of the drive spring.

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.

Methods and systems for controlling a driving feature for an automated driving system are provided. In one embodiment, a method includes: receiving a first sensor signal from a first sensor; receiving a second sensor signal from a second sensor; selectively determining a driver intent based on at least one of the first sensor signal and the second sensor signal; and controlling the driving feature based on the driver intent.

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.

An occupant support adapted for use in a vehicle includes a seat bottom, a seat back, and a sensory system. The seat bottom is coupled to a floor of the vehicle. The seat back extends upwardly away from the seat bottom. The sensor system is configured to monitor for fatigue of an occupant of the occupant support.

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.

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.

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).

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.