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.

A road surface condition assessing device includes: a tire-mounted device; and a vehicle body system. The tire-mounted device includes: a vibration detector that outputs a detection signal of a vibration on a tire; a waveform processor that generates the road surface data; and a first data communication unit. The vehicle body system includes: a second data communication unit; and a road surface evaluation unit that evaluates the road surface condition. The tire-mounted device transmits an advertise signal including the road surface data indicative of a result of a waveform process on the detection signal and a waveform processing value corresponding to the road surface condition. The vehicle body system evaluates the road surface condition based on the waveform processing value.

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.

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.

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.

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 system for navigating a host vehicle may include memory and at least one processor configured to receive a plurality of images acquired by a camera onboard the host vehicle; generate, based on analysis of the plurality of images, a road geometry model for a segment of road forward of the host vehicle; determine, based on analysis of at least one of the plurality of images, one or more indicators of an orientation of the host vehicle; and generate, based on the one or more indicators of orientation of the host vehicle and the road geometry model for the segment of road forward of the host vehicle, one or more output signals configured to cause a change in a pointing direction of a movable headlight onboard the host vehicle.

Kalman filter based road grade estimation techniques use models of a longitudinal accelerometer, an angular pitch rate gyroscope, and a velocity sensor and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The proposed Kalman filter formulation is unique in that it uses a mathematical model of the sensors where the gyroscope output is considered as the input and the combined accelerometer and velocity sensor output is considered as the output of the model whose states are to be estimated. By using this unique second-order state space model, a Kalman filter based estimation algorithm is developed to estimate road grade accurately in real-time. This estimated road grade is then being utilized by various vehicle efficiency and/or safety systems to improve vehicle efficiency and/or vehicle safety.

A sensor device for detecting moisture on a roadway of a vehicle includes a housing having at least one flat housing area. The housing is constructed as a resonant body and is provided for mounting in a wheel arch of the vehicle. At least one structure-borne sound sensor is arranged in the housing area of the housing and at least one connecting means is assigned to the housing for producing a connection between the housing and the wheel arch. The at least one connecting means is constructed to be vibration damping and constructed for receiving a decoupling of the sensor device and the wheel arch. The at least one connecting means is constructed for producing a connection between the housing and the wheel arch and the structure-borne sound sensor is configured to detect only structure-borne sound signals caused by impacting moisture droplets on the housing.

Kalman filter based road grade estimation techniques use models of a longitudinal accelerometer, an angular pitch rate gyroscope, and a velocity sensor and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The proposed Kalman filter formulation is unique in that it uses a mathematical model of the sensors where the gyroscope output is considered as the input and the combined accelerometer and velocity sensor output is considered as the output of the model whose states are to be estimated. By using this unique second-order state space model, a Kalman filter based estimation algorithm is developed to estimate road grade accurately in real-time. This estimated road grade is then being utilized by various vehicle efficiency and/or safety systems to improve vehicle efficiency and/or vehicle safety.