Abstract: A method of diagnosing a root cause for an exhibited vehicle failure, comprises initiating a vehicle health management (VHM) algorithm, repeatedly monitoring, at a specified time interval, the state of health (SOH) for at least one vehicle component, wherein the SOH for the at least one vehicle component is one of Green (normal operation), Yellow, and Red, calculating a number of consecutive Green SOH results for the at least one vehicle component, and providing an indication of likelihood that the at least one vehicle component is not a root cause of the exhibited vehicle failure based on the number of consecutive Green SOH results for the at least one vehicle component.

Abstract: A method of diagnosing a root cause for an exhibited vehicle failure, comprises initiating a vehicle health management (VHM) algorithm, repeatedly monitoring, at a specified time interval, the state of health (SOH) for at least one vehicle component, wherein the SOH for the at least one vehicle component is one of Green (normal operation), Yellow, and Red, calculating a number of consecutive Green SOH results for the at least one vehicle component, and providing an indication of likelihood that the at least one vehicle component is not a root cause of the exhibited vehicle failure based on the number of consecutive Green SOH results for the at least one vehicle component.

Abstract: A method for planning and adapting a recommended travel route to a route destination for a vehicle having various subsystems includes identifying the route destination and receiving vehicle health management (VHM)/state of health (SOH) information for each subsystem. The vehicle with a controller programmed to execute the method is also disclosed. The method includes calculating route characteristics of candidate travel routes to the destination using the VHM information and determining, from among the candidate travel routes, travel routes for which the characteristics meet a respective threshold requirement. Thereafter, the controller executes a control action by displaying a candidate route meeting the threshold requirements. An occupant is prompted to revise the mission requirements when no candidate route exists. A default route to a designated parking location or repair depot may be displayed when none of the candidate routes meet the threshold requirements.

Abstract: A method proactively transitions performance of a functional operation from a primary subsystem to a secondary subsystem within a vehicle or other system having an electronic control unit (ECU). The method includes receiving health management information via the ECU when the primary subsystem is actively performing the functional operation within the system and the secondary subsystem operates in a standby mode, wherein the health information is indicative of a numeric state of health (SOH) of the primary subsystem. The method also includes comparing the numeric SOH to a calibrated non-zero threshold SOH, and then commanding, via the ECU, a transition of the performance of the functional operation to the secondary subsystem and placing the primary subsystem in the standby mode when the numeric SOH is less than the calibrated non-zero threshold SOH. A vehicle executes the method via the ECU.

Abstract: Systems and method are provided for collaboration between autonomous vehicles. In one embodiment, a processor-implemented method for coordinating travel between multiple autonomous vehicles is provided. The method includes sending a collaboration request to one or more vehicles in an area to form a group to perform a mission, receiving an acceptance of the collaboration request to join the group wherein the group includes a plurality of vehicles, cooperating in assigning leading functions for the group to one or more of the plurality of vehicles in the group, cooperating in mission negotiations for the group, cooperating in determining a formation for the group, and cooperating in generating a trajectory for the group. The vehicles in the group are operated in accordance with the determined formation and generated trajectory.

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 method proactively transitions performance of a functional operation from a primary subsystem to a secondary subsystem within a vehicle or other system having an electronic control unit (ECU). The method includes receiving health management information via the ECU when the primary subsystem is actively performing the functional operation within the system and the secondary subsystem operates in a standby mode, wherein the health information is indicative of a numeric state of health (SOH) of the primary subsystem. The method also includes comparing the numeric SOH to a calibrated non-zero threshold SOH, and then commanding, via the ECU, a transition of the performance of the functional operation to the secondary subsystem and placing the primary subsystem in the standby mode when the numeric SOH is less than the calibrated non-zero threshold SOH. A vehicle executes the method via the ECU.

Abstract: Systems and method are provided for collaboration between autonomous vehicles. In one embodiment, a processor-implemented method for coordinating travel between multiple autonomous vehicles is provided. The method includes sending a collaboration request to one or more vehicles in an area to form a group to perform a mission, receiving an acceptance of the collaboration request to join the group wherein the group includes a plurality of vehicles, cooperating in assigning leading functions for the group to one or more of the plurality of vehicles in the group, cooperating in mission negotiations for the group, cooperating in determining a formation for the group, and cooperating in generating a trajectory for the group. The vehicles in the group are operated in accordance with the determined formation and generated trajectory.

Abstract: A method and associated device for automated maintenance scheduling for an autonomous vehicle includes monitoring a state of health (SOH) of an on-vehicle subsystem and monitoring an appointment log associated with a vehicle operator. Upon detecting a change in the SOH of the on-vehicle subsystem, the method includes communicating with a service center to determine a recommended maintenance action associated with the change in the SOH of the on-vehicle subsystem and to determine a proposed service appointment for the autonomous vehicle to effect the recommended maintenance action. The proposed service appointment is coordinated with the appointment log, and verified with the vehicle operator. A service appointment is scheduled based upon the proposed service appointment when verified by the vehicle operator. The appointment log is updated to include the service appointment.

Abstract: A vehicle control system includes a plurality of components that are disposed to effect a vehicle function and a controller in communication with the plurality of components. A method for controlling the vehicle includes monitoring states of health (SOHs) of the plurality of components. Upon detecting a change in status of the SOH of the subject component, an allowable window of operation for the subject component and an allowable window of operation for the related component are determined, and operating constraints related to the vehicle function are determined based upon the allowable windows of operation for the subject component and the related component. Operation of the vehicle is controlled subject to the operating constraints related to the vehicle function.

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 method for planning and adapting a recommended travel route to a route destination for a vehicle having various subsystems includes identifying the route destination and receiving vehicle health management (VHM)/state of health (SOH) information for each subsystem. The vehicle with a controller programmed to execute the method is also disclosed. The method includes calculating route characteristics of candidate travel routes to the destination using the VHM information and determining, from among the candidate travel routes, travel routes for which the characteristics meet a respective threshold requirement. Thereafter, the controller executes a control action by displaying a candidate route meeting the threshold requirements. An occupant is prompted to revise the mission requirements when no candidate route exists. A default route to a designated parking location or repair depot may be displayed when none of the candidate routes meet the threshold requirements.

Abstract: A system and method that allow non-human autonomous agents to participate in a social internet network. The system defines the autonomous agent by a standard email address, website address, or other such identification, that allows human participants of the social network to contact the non-human autonomous agent to perform some operation through the Internet. In one non-limiting example, the non-human autonomous agent is a vehicle that allows the vehicle owner, or user, to contact the vehicle through the social network to perform some operation, such as unlocking the vehicle doors, and allow authorized participants of the social network to see the vehicle as a participant of the network to gain information therefrom, such as the location of the vehicle.

Abstract: A system and method that allow non-human autonomous agents to participate in a social internet network. The system defines the autonomous agent by a standard email address, website address, or other such identification, that allows human participants of the social network to contact the non-human autonomous agent to perform some operation through the Internet. In one non-limiting example, the non-human autonomous agent is a vehicle that allows the vehicle owner, or user, to contact the vehicle through the social network to perform some operation, such as unlocking the vehicle doors, and allow authorized participants of the social network to see the vehicle as a participant of the network to gain information therefrom, such as the location of the vehicle.

Abstract: A vehicle repair assist system for repairing a vehicle fault in a vehicle. A symptom input module is provided for entering vehicle symptom information relating to the fault. A diagnostic code module retrieves diagnostic trouble codes from the vehicle. The diagnostic trouble codes are generated in response to a fault occurrence. A knowledge-based fault module identifies potential causes of the vehicle fault based on at least one of the symptom information and diagnostic trouble codes. A repair assistant module identifies recommended repair parts and repair procedures for repairing the cause of the vehicle fault. The repair assistant module communicates with the knowledge-based fault module for obtaining a prioritized listing of the recommended repair parts and repair procedures for repairing the vehicle fault.

Abstract: A method for correcting service manual textual inconsistencies. Extracting textual procedures from service documents stored in a memory of a service document storage device. Each term of an extracted textual procedure terminology is compared to a correlating target name terminology for identifying any matching terms by a processor. An overlap similarity is computed as a function of the identified matching terms from the extracted textual procedure terminology and the correlating target name terminology. A determination is made whether the overlap similarity is greater than a predetermined similarity threshold. The service documents are modified to change the extracted textual procedure terminology to the correlating target name terminology in response to the overlap similarity being greater than the predetermined similarity threshold and the extracted textual procedure terminology not exactly matching the correlating target name terminology.

Abstract: A method for correcting service manual textual inconsistencies. Extracting textual procedures from service documents stored in a memory of a service document storage device. Each term of an extracted textual procedure terminology is compared to a correlating target name terminology for identifying any matching terms by a processor. An overlap similarity is computed as a function of the identified matching terms from the extracted textual procedure terminology and the correlating target name terminology. A determination is made whether the overlap similarity is greater than a predetermined similarity threshold. The service documents are modified to change the extracted textual procedure terminology to the correlating target name terminology in response to the overlap similarity being greater than the predetermined similarity threshold and the extracted textual procedure terminology not exactly matching the correlating target name terminology.

Abstract: A vehicle fault diagnosis and prognosis system includes a computing platform configured to receive a classifier from a remote server, the computing platform tangibly embodying computer-executable instructions for evaluating data sequences received from a vehicle control network and applying the classifier to the data sequences, wherein the classifier is configured to determine if the data sequences define a pattern that is associated with a particular fault.

Abstract: A method and system for comparing and merging fault models which are derived from different data sources. Two or more fault models are first represented as bipartite weighted graphs, which define correlations between failure modes and symptoms. The nodes of the graphs are compared to find failure modes and symptoms which are the same even though the specific terminology may be different. A graph matching method is then used to compare the graphs and determine which failure mode and symptom correlations are common between them. Finally, smoothing techniques and domain expert knowledge are used to merge and update the fault models, producing an integrated fault model which can be used by onboard vehicle systems, service facilities, and others.

Abstract: A method for verifying and improving a vehicle fault model is disclosed. The method includes analyzing the available field failure data that includes vehicle symptoms and failures for many vehicles. The method performs an analysis using the field failure data that includes using subject matter expert knowledge to determine the most significant failure modes and the most significant symptoms. The method also includes learning simulation parameters from the field failure data and simulating faults using the learned simulation parameters. The method further includes verifying and validating the fault model based on the most significant failure modes and the most significant symptoms from the what-if analysis and the faults identifies by the simulation, and using a diagnostic reasoner to analyze the revised fault model to generate estimated faults. The method then compares the estimated faults to the simulated faults to determine true detection and false alarm rates for a benefit analysis.