Managing a brake system of a vehicle includes collecting sensor data from one or more sensors in or around the vehicle, calculating brake effectiveness values based on the sensor data, calibrating the brake effectiveness values based on environmental context data associated with the vehicle, accumulating the calibrated brake effectiveness values as a dataset, generating a prediction curve or formula based the dataset, and scheduling a maintenance alarm for the brake system based on the brake effectiveness values.

A method for managing a vehicle brake system includes collecting sensor data from one or more sensors in or around the vehicle, calculating brake effectiveness values based on the sensor data, calibrating the brake effectiveness values based on environmental context data associated with the vehicle, accumulating the calibrated brake effectiveness values as a dataset, generating a prediction curve or formula based the dataset, and scheduling a maintenance alarm for the brake system based on the brake effectiveness values.

The invention relates to a brake system comprising at least one brake with a frictional surface, a pad carrier with a brake pad, and a control and monitoring unit. According to the invention, the brake system additionally comprises a brake temperature sensor which is connected to the control and monitoring unit that is designed to ascertain brake temperature maintenance values on the basis of the brake effectiveness request, to compare a temperature ascertained by the brake temperature sensor with corresponding brake temperature maintenance values, and to ascertain at least one correction factor on the basis of a deviation. The control and monitoring unit is additionally designed to correct the brake control signal by the at least one correction factor and to actuate the controller using the corrected brake control signal.

A lane-control system suitable for use on an automated vehicle comprising a camera, a lidar-sensor, and a controller. The camera captures an image of a roadway traveled by a host-vehicle. The lidar-sensor detects a discontinuity in the roadway. The controller is in communication with the camera and the lidar-sensor and defines an area-of-interest within the image, constructs a road-model of the roadway based on the area-of-interest, determines that the host-vehicle is approaching the discontinuity, and adjusts the area-of-interest within the image based on the discontinuity.

A road surface condition estimation device extracts a detection signal of a vibration power generation element during a ground contact section to detect a road surface condition. A threshold used for determination of the ground contact section is variable according to a traveling speed of a vehicle. As a result, even if a pulse level of an output voltage of the vibration power generation element changes according to the traveling speed of the vehicle, the threshold corresponding to the change can be set. The ground contact section is determined with the use of the above thresholds, thereby being capable of performing the determination with high accuracy. Therefore, the road surface condition can be detected with high accuracy based on the ground contact section determined with high accuracy.

Disclosed is a controller for controlling brake action of an aircraft based on a current runway condition of the runway. The controller receives an expected condition of the runway and expected brake action for the aircraft from air traffic control, or other entity. The controller sends actual measured brake pressure applied by the aircraft while landing, along with sensor data from sensors on the aircraft configured to detect contaminants such as fluid or ice on the runway to a machine learning model. The machine learning model is configured to output a predicted brake action for the aircraft and predicted runway condition. The controller compares the expected brake action and expected runway condition to the predicted brake action and predicted runway condition. If there is any discrepancy, the controller sends the predicted brake action and runway condition to air traffic control to update the expected brake action and expected runway condition.

Provided is a monitoring apparatus including an acquiring unit that acquires information relating to an operation status when an operating body is operated, a determination unit that determines one of plural categories of the operation status, which are classified based on a degree of occurrence of a malfunction of the operating body or a degree of danger of the operation status, to which the information relating to the operation status acquired by the acquiring unit belongs, and an attention calling unit that calls for an attention for an operation of the operating body in a case where the information relating to the operation status is determined to belong to a specific category.

A lane-control system suitable for use on an automated vehicle comprising a camera, a lidar-sensor, and a controller. The camera captures an image of a roadway traveled by a host-vehicle. The lidar-sensor detects a discontinuity in the roadway. The controller is in communication with the camera and the lidar-sensor and defines an area-of-interest within the image, constructs a road-model of the roadway based on the area-of interest, determines that the host-vehicle is approaching the discontinuity, and adjusts the area-of-interest within the image based on the discontinuity.

A method of determining a surface condition of a path of travel. A plurality of images is captured of a surface of the path of travel by an image capture device. The image capture device captures images at varying scales. A feature extraction technique is applied by a feature extraction module to each of the scaled images. A fusion technique is applied, by the processor, to the extracted features for identifying the surface condition of the path of travel. A road surface condition signal provide to a control device. The control device applies the road surface condition signal to mitigate the wet road surface condition.

The present disclosure relates to a support braking apparatus and method for a vehicle capable of detecting braking target candidates for a vehicle, matching a braking amount required for the detected braking target candidates with a braking amount of a driver to select a specific braking target, and braking a subject vehicle by following up braking required for the selected braking target even if the driver does not maintain a braking control for the selected braking target. A support braking apparatus for a vehicle according to an embodiment of the present disclosure includes: a braking amount measurer configured to measure a first braking amount based on signals input from an acceleration sensor and a wheel sensor; a braking target detector configured to detect braking target candidates on a driving route by a camera and a radar; a braking target selector configured to match a first braking amount with a second braking amount to stop a subject vehicle within a braking range for the braking target candidates to select the braking target, and a braking controller configured to control an automatic braking to stop the subject vehicle within the braking range for the selected braking target.