Techniques are described in which sensor data is used to determine one or more of background noise or occupancy associated with a passenger cabin of a vehicle. The sensor data, in turn, is used to determine an operating state for one or more components of the vehicle (e.g., pumps, compressors, fans, blowers, etc.) such that an amount of background noise within the passenger cabin is reduced (e.g., when a passenger/occupant is present). In various examples, the operating state of the component may operate in a different, though louder, state (e.g., higher efficiency, greater power, etc.) when an occupant is not present or proximate the component.

A method for estimating a friction between a road surface and a tire of a steered wheel of a vehicle. The steered wheel being fit with dynamic steering. The vehicle includes a steering wheel and a set of sensors comprising wheel end sensors and steering wheel sensors configured to measure signals corresponding to a set of parameters., The steering wheel parameters comprising at least a steering wheel torque and a steering wheel angle. The method comprising the following steps implemented by the electronic control unit collect the signals, corresponding to the set of parameters, measured by the sensors during a period of time; process, by the signal processing module, the signals collected to provide processed signal data provide the processed signal data as input to the wheel end friction estimation model, the wheel end friction estimation model being configured to output a friction estimation of the friction between the road surface and the tire of the wheel.

A maneuvering assistant system for a vehicle combination comprising an interface configured to receive measurements from a camera, processing circuitry implementing a Kalman filter configured to observe an articulation angle of the vehicle combination on the basis of the measurements from the camera, a signal analyzer configured to repeatedly estimate a signal noise of the camera measurement and to adjust the Kalman filter on the basis of statistics derived from the estimated signal noise, and an assistance unit configured to receive an estimate of the articulation angle from the Kalman filter and provide drive actions based on the estimate.

The present disclosure provides a method of estimating a weight of a vehicle, including determining whether a current state is an enable state in which a vehicle stoppage event due to brake occurs from vehicle driving information, by a weight estimating system, when the current state is the enable state, removing noise by filtering an acceleration signal input from an acceleration sensor at a moment when the vehicle is stopped, by the weight estimating system, determining a period value of an acceleration signal from the acceleration signal from which noise is removed, by the weight estimating system, and estimating the weight of the vehicle using information on the determined period value, by the weight estimating system.

A road surface condition estimation apparatus which accurately estimates a road surface condition even under changes of external environment such as weather, etc., and a road surface condition estimation method using the same is described. The road surface condition estimation apparatus includes: a sensor module which is mounted on a tire; a receiver module which receives sensing information measured by the sensor module; a processing module which extracts a parameter for estimating a road surface condition by analyzing the sensing information received by the receiver module; and an estimation module which estimates the road surface condition by using the parameter extracted by the processing module. The sensing information includes an acceleration of the tire.

A system for detection of a target sound in an environment of a vehicle, includes an audio sensor, a computer processor, and a memory storing a digital target sound template produced by converting a sample of the target sound in accordance with conversion parameters. The computer processor receives a sound signal from the audio sensor, digitizes the sound signal in accordance with the conversion parameters, and determines a degree of similarity between the digitized signal and the digital target sound template. The sound signal may be logarithmically amplified before being digitized. The sound signal may be received from two audio sensors, and the direction of the target sound may be determined based on a difference between time indices for detection of the target sound for each audio sensor.

A system and method are disclosed for overheight vehicle impact avoidance and incident detection. A detection subsystem of the system determines whether an incoming vehicle is clear to pass, depending at least on its height. The detection subsystem relies on distance sensors located along the freeway to detect the highest point of the vehicle and outputs an indication of whether the vehicle can pass. A warning subsystem receives the indication that a vehicle is overheight and provides a warning indication to the driver about the vehicle's inability to pass an upcoming structure. The warning subsystem may also perform a variety of other tasks, such as, for example, logging a variety of information about the vehicle and the incident, sending data about the incident locally to a central command, and generating reports.

A system controls a vehicle. The vehicle implements at least one control application using a variable measured by at least one sensor of a perception system installed in the vehicle. The control system includes an adaptive controller to dynamically activate one or more elementary controllers of a set of elementary controllers including at least two elementary controllers, each elementary controller can apply a control function of a vehicle parameter acting on actuators of the vehicle, the control functions being separate, based on a precision indicator of the perception system determined according to a real-time value of the variable.

A system that determines a road surface profile based upon filter coefficient data from noise cancellation systems is disclosed. The system includes a filter coefficient monitoring module that is configured to receive a first set of filter coefficient data from an noise cancellation module. The system also includes a road surface profile module that is configured to receive an input representing a road surface type and generate a road surface profile based upon the road surface type and the first set of filter coefficient data and to store the road surface profile including a correspondence between the road surface type and the first set of filter coefficient data.

A system that determines a road surface profile based upon filter coefficient data from noise cancellation systems is disclosed. The system includes a filter coefficient monitoring module that is configured to receive a first set of filter coefficient data from an noise cancellation module. The system also includes a road surface profile module that is configured to receive an input representing a road surface type and generate a road surface profile based upon the road surface type and the first set of filter coefficient data and to store the road surface profile including a correspondence between the road surface type and the first set of filter coefficient data.