V2X-Connected Cooperative Diagnostic & Prognostic Applications in Vehicular AD HOC Networks

• US 20110238259A1
• Filed: 03/25/2010
• Published: 09/29/2011
• Est. Priority Date: 03/25/2010
• Status: Abandoned Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (Motors Liquidation Co. GUC Trust)
• Inventors: Zhang, Yilu, Bai, Fan
• Application Number:
  US12/731,395
• Publication Number:
  US 20110238259A1
• Time in Patent Office: Days
• US Class Current: 701/33
• CPC Class Codes: H04L 67/125 involving control of end-device applications over a network
• CPC Class Codes: H04W 24/00 Supervisory, monitoring or testing arrangements
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A method is provided for processing and analyzing diagnostic and prognostic data in a vehicle ad-hoc network. Diagnostic and prognostic data is exchanged between a host vehicle and remote vehicles in the vehicle ad-hoc network. The received diagnostic and prognostic data is stored in a memory of the host vehicle. Redundancy is eliminated in the received diagnostic and prognostic data. The diagnostic and prognostic data is assigned to clusters. Anomalies are detected in the stored data utilizing clustering techniques that determine whether a cluster of diagnostic and prognostic data formed from the host vehicle substantially deviates from the clusters of diagnostic and prognostic data formed from the remote vehicles. A driver of a vehicle is notified if the cluster data from a host vehicle deviates significantly from the clusters from the remote vehicles.

Claims

1. A method of processing and analyzing diagnostic and prognostic data in a vehicle ad-hoc network, the method comprising the steps of:

exchanging diagnostic and prognostic data between a host vehicle and remote vehicles in the vehicle ad-hoc network;

storing the received diagnostic and prognostic data in a memory of the host vehicle;

eliminating redundancy in the received diagnostic and prognostic data;

detecting anomalies in the stored data utilizing a clustering technique that determines whether a cluster of diagnostic and prognostic data formed from the host vehicle substantially deviates from the clusters of diagnostic and prognostic data formed from the remote vehicles; and

notifying a driver of a vehicle if the cluster data from a host vehicle deviates significantly from the clusters from the remote vehicles.

2. The method of claim 1 wherein a hash function based probabilistic counting technique is used to reduce redundancies.

3. The method of claim 4 wherein the hash function based probabilistic counting technique includes Flajolet-Martin sketch logic.

4. The method of claim 1 wherein a dedicated short range communication protocol is used as a communication channel between the host vehicle and remote vehicles.

5. The method of claim 1 wherein WiFi is used to communicate between the host vehicle and remote vehicles.

6. The method of claim 1 wherein the diagnostic and prognostic data includes operation and fault data from remote vehicles.

7. The method of claim 1 wherein the step of detecting anomalies further comprises:

estimating a center of each respective cluster as diagnostic and prognostic data is assigned to each respective cluster;

determining whether the centers of each respective cluster of the remote vehicles converge with one another; and

determining whether the diagnostic and prognostic data in the cluster of the host vehicle substantially deviates from diagnostic and prognostic data in the clusters of the remote vehicle in response to centers of each respective cluster of the remote vehicles converging.

8. The method of claim 7 wherein determining whether the cluster of the host substantially deviates from the cluster of the remote vehicle includes determining whether the clusters between the host vehicle and the remote vehicles deviate by a predetermined threshold.

9. The method of claim 8 the predetermined threshold is a calculated standard deviation.

10. The method of claim 8 the predetermined threshold is a factor of the calculated standard deviation.

11. The method of claim 1 wherein assigning the diagnostic and prognostic data to clusters further comprises the steps of:

calculating a distance from a respective data point to each cluster center;

determining the respective cluster center that is the minimum distance from the respective data point; and

assigning the respective data point to the cluster having the cluster center that is the minimum distance from the respective data point.

12. The method of claim 1 wherein the anomaly includes a current failure in an operating parameter of the vehicle.

13. The method of claim 1 wherein the anomaly includes a predicted failure in an operating parameter of the vehicle.

14. The method of claim 1 wherein the anomaly is provided to a centralized diagnostic and prognostic reporting system.

15. The method of claim 1 wherein the centralized diagnostic and prognostic reporting system performs error checking.

16. The method of claim 1 wherein the centralized diagnostic and prognostic reporting system performs notifies the driver of the vehicle.

17. The method of claim 1 wherein a human machine interface within the vehicle notifies the driver of the anomaly.