Abstract:
A method for diagnosing a fault mode in a system includes recording a hierarchical precedence rule assigning a priority level to fault modes of the system, and recording, in a fault report matrix, fault reports indicative of a corresponding one or more of the fault modes. The method also includes using the hierarchical precedence rule to determine the assigned relative priority level for the fault reports in response to a predetermined condition, e.g., a requested engine starting event, and identifying a root cause subsystem as a subsystem having the highest assigned priority level. A control action executed via the controller identifies the root cause subsystem by recording a diagnostic code and/or transmitting a message. The system is also disclosed, as is a computer-readable medium programmed with instructions embodying the method.
Abstract:
A controller area network (CAN) includes a plurality of CAN elements comprising a communication bus and a plurality of controllers. A method for monitoring includes periodically determining vectors wherein each vector includes inactive ones of the controllers detected during a filtering window. Contents of the periodically determined vectors are time-filtered to determine a fault record vector. A fault on the CAN is isolated by comparing the fault record vector and a fault signature vector determined based upon a network topology for the CAN.
Abstract:
A method for monitoring controller area network (CAN) on a mobile system includes identifying links and associated nodes between all the nodes of the CAN, and ranking all the links according to their order of connection to the monitoring controller, including assigning lower ranks to ones of the links proximal to the monitoring controller and assigning higher ranks to ones of the links distal to the monitoring controller. For each of said links, the associated node distal to the monitor is identified. The on-board monitoring controller determines a fault signature for each of the links starting with the link having the highest ranking, said fault signature comprising identified ones of the associated nodes distal to the monitor for each of the corresponding links.
Abstract:
A system for designing a driving system for a vehicle. The system includes a first sensor, a second sensor and a processor. The first sensor is configured to obtain an external parameter indicative of a lane marking during a time period in which an automated driving program is being operated at the vehicle. The second sensor is configured to obtain an internal parameter of the vehicle during the time period. The processor is configured to detect a lane touch event occurring during the time period, determine an error occurring in the driving system resulting in the lane touch event based on the internal parameter and the external parameter, identify a failure domain of the driving system in which the error occurs, and change a design of a program of the driving system related to the failure domain.
Abstract:
A vehicle, system method for operating the vehicle is disclosed. The system includes a camera and a processor. The camera is configured to obtain a camera image of a road segment. The processor determines a location of a road edge for the road segment within the camera image, obtains a lane attribute for the road segment, generates a virtual lane mark for the road segment based on the road edge and the lane attribute, and moves the vehicle along the road segment by tracking the virtual lane mark.
Abstract:
A method of root cause diagnosis of fault data from a vehicle includes identifying a first vehicle fault and selecting from field repair data a vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying from the field repair data an effective repair of the identified first vehicle fault. The method additionally includes training and testing via a machine learning algorithm, a labor code classifier using the identified effective repair of the first vehicle fault and the selected vehicle feature corresponding to the identified first vehicle fault. The method also includes identifying and classifying, using the trained classifier, indistinguishable labor codes. Furthermore, the method includes communicating the identified and classified indistinguishable labor codes for diagnosing a root cause of real time first vehicle fault data. A computer-readable medium storing an executable computer algorithm for performing the root cause diagnosis of vehicle fault data is also envisioned.
Abstract:
A method and apparatus detecting battery drain are provided. The method includes: collecting critical data parameters from a plurality of electronic controller units (ECUs); identifying at least one ECU that is active from among the plurality of ECUs based on the critical data parameters and storing ECU snapshot data of the identified at least one ECU; determining an activity of the identified at least one ECU based on the ECU snapshot data and the critical data parameters; determining battery drain information based on at least one from among the ECU snapshot data, the critical data parameters, the activity of the identified at least one ECU, current draw information and fleet information; and outputting determined battery drain information. The method may be used to determine and isolate a root cause of battery drain by analyzing messages over a vehicle network.
Abstract:
A method is used to evaluate a camera-related subsystem in a digital network, e.g., aboard a vehicle or fleet, by receiving, via a camera diagnostic module (CDM), sensor reports from the subsystem and possibly from a door sensor, rain/weather sensor, or other sensor. The CDM includes data tables corresponding to subsystem-specific fault modes. The method includes evaluating performance of the camera-related subsystem by comparing potential fault indicators in the received sensor reports to one of the data tables, and determining a pattern of fault indicators in the reports. The pattern is indicative of a health characteristic of the camera-related subsystem. A control action is executed with respect to the digital network in response to the health characteristic, including recording a diagnostic or prognostic code indicative of the health characteristic. The digital network and vehicle are also disclosed.
Abstract:
A controller architecture for monitoring an autonomic vehicle control system includes a first controller, a second controller, a telematics controller, a third controller, a plurality of subsystem controllers, a first and a second communication bus, and a first and a second communication link. The telematics controller in communication with the first controller. The second controller includes a second processor and a second memory device. Each subsystem controller is configured to effect operation of one of a subsystem, wherein each of the subsystem controllers includes a vehicle health monitor (VHM) agent. The third controller includes a third processor and a third memory device. A first instruction set includes a prognostic classification routine based upon inputs from the VHM agents of the plurality of subsystem controllers. The telematics controller is disposed to communicate an output from the prognostic classification routine to an off-board controller.
Abstract:
A perception module of a spatial monitoring system to monitor and characterize a spatial environment proximal to an autonomous vehicle is described. A method for evaluating the perception module includes capturing and storing a plurality of frames of data associated with a driving scenario for the autonomous vehicle, and executing the perception module to determine an actual spatial environment for the driving scenario, wherein the actual spatial environment for the driving scenario is stored in the controller. The perception module is executed to determine an estimated spatial environment for the driving scenario based upon the stored frames of data associated with the driving scenario, and the estimated spatial environment is compared to the actual spatial environment for the driving scenario. A first performance index for the perception module is determined based upon the comparing, and a fault can be detected.