Abstract:
A method and system for estimating the state of health of an object sensing fusion system. Target data from a vision system and a radar system, which are used by an object sensing fusion system, are also stored in a context queue. The context queue maintains the vision and radar target data for a sequence of many frames covering a sliding window of time. The target data from the context queue are used to compute matching scores, which are indicative of how well vision targets correlate with radar targets, and vice versa. The matching scores are computed within individual frames of vision and radar data, and across a sequence of multiple frames. The matching scores are used to assess the state of health of the object sensing fusion system. If the fusion system state of health is below a certain threshold, one or more faulty sensors are identified.
Abstract:
A system for a vehicle includes a plurality of sensors onboard the vehicle and a controller. A first sensor of the plurality of sensors is configured to detect lane markings on a roadway. The controller is configured to store data from the plurality of sensors. In response to receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings, respectively, based on the stored data; isolate one of the causes as a root cause for the misdetection of lane markings; and provide a response for mitigating the misdetection of lane markings on the roadway based on the root cause for the misdetection of lane markings.
Abstract:
A controller processes data from one or more sensors of a subsystem of a vehicle. The processing includes smoothing the data and calculating a mean of the data. The controller identifies a transition point in the processed data where a moving average of the data is less than the mean by a predetermined amount indicating a trend. The controller selects a segment of the processed data subsequent to the transition point, detects the trend in the segment using regression, and extrapolates the detected trend to reach a predetermined fault threshold. The controller predicts a failure of the subsystem based on a slope of the extrapolated trend and provides an indication of the prediction based on the slope to schedule a service for the subsystem.