Fleet-Wide Monitoring System for Vehicles

• US 20180039558A1
• Filed: 08/03/2016
• Published: 02/08/2018
• Est. Priority Date: 08/03/2016
• Status: Active Grant

• Current Assignee: DENSO Corporation, Runtime Verification Inc., Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Original Assignee: DENSO Corporation, Runtime Verification Inc., Toyota Infotechnology Center USA, Inc.
• Inventors: Shiraishi, Shinichi, Rosu, Grigore, Daian, Philip, Iwai, Akihito, Mae, Yuki
• Granted Patent: US 11,048,610 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/3013 where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems testing or monitoring of control systems or parts thereof G05B23/02
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 11/3608 using formal methods, e.g. model checking, abstract interpretation theorem proving G06N5/013
• CPC Class Codes: G06F 11/3612 by runtime analysis performance monitoring G06F11/3466
• CPC Class Codes: G06F 11/3636 by tracing the execution of the program
• CPC Class Codes: G06F 11/366 using diagnostics G06F11/0703 takes precedence
• CPC Class Codes: G06F 2201/865 Monitoring of software
• CPC Class Codes: G06F 8/65 Updates security arrangements therefor G06F21/57
• CPC Class Codes: G07C 5/006 Indicating maintenance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/0825 using optical means

Abstract

The disclosure includes a system, method and tangible memory for providing a monitoring system for a fleet of vehicles. The method may include a monitoring server receiving a set of trace data associated with a vehicle application. The fleet of vehicles may include a plurality of vehicles that are communicatively coupled to the monitoring server via a wireless network. Each of the vehicles may include a copy of the vehicle application. The set of trace data may describe operations that are executed responsive to an onboard computer executing the copy of the vehicle application. The method may include inputting the set of trace data and model data into an RV-Predict application. The model data may describe a formal model of the vehicle application. The method may include executing the RV-Predict application with a processor to generate predictive data describing a predictive analysis of whether the vehicle application includes an error.

Claims

1. A method comprising:

receiving a set of trace data from a plurality of vehicles included in a fleet of vehicles, wherein the plurality of vehicles includes a copy of a vehicle application and the set of trace data describes one or more operations that are executed responsive to an onboard vehicle computer executing the copy of the vehicle application;

inputting the set of trace data and model data into an RV-Predict application, wherein the model data describes a formal model of the vehicle application; and

executing the RV-Predict application with a processor to generate predictive data describing a predictive analysis of whether the vehicle application includes an error, wherein the predictive analysis is configured to never include a false positive.

2. The method of claim 1, wherein each vehicle included in the fleet of vehicles includes a copy of the vehicle application.

3. The method of claim 1, wherein the predictive analysis is proactive and occurs before the error is identifiable by the operation of the fleet of vehicles.

4. The method of claim 1, wherein the copy of the vehicle application includes a monitor module that includes software that is operable to collect trace data describing the one or more operations responsive to the copy of the vehicle application and the monitor module being executed by the onboard vehicle computer.

5. The method of claim 1, wherein the vehicle application is selected from a set that includes one or more of the following:

a vehicle navigation application including a monitor module operable to collect the trace data responsive to being executed by the onboard computer;

an advanced driver assistance system application including the monitor module;

an infotainment system application including the monitor module;

a diagnostic application including the monitor module; and

an Automotive Open System Architecture (“

AUTOSAR”

) application including the monitor module.

6. The method of claim 1, wherein the fleet of vehicles shares a common manufacturer.

7. The method of claim 1, wherein the fleet of vehicles shares a common model.

8. The method of claim 1, wherein the fleet of vehicles shares a common original equipment manufacturer (“

OEM”

).

9. The method of claim 1, wherein the formal model describes a set of desired behaviors of the vehicle application and the RV-Predict application, responsive to being executed by the processor, compares the set of trace data to the set of desired behaviors of the vehicle application to provide the predictive analysis described by the predictive data.

10. The method of claim 1, wherein the method is executed by a computing device that is wirelessly coupled to the plurality of the vehicles included in the fleet of vehicles via a wireless network.

11. The method of claim 10, further comprising:

determining, based on the predictive data, that the vehicle application includes the error;

generating a patch that is operable to be installed in the copy of the vehicle application to correct the error; and

distributing the patch to one or more of the vehicles included in the fleet of vehicles.

12. The method of claim 11, wherein distributing the patch includes wirelessly transmitting patch data describing the patch to the plurality of vehicles included in the fleet of vehicles via the wireless network.

13. The method of claim 12, further comprising the plurality of vehicles receiving the patch data via the wireless network and updating the copy of the vehicle application based on the patch data.

14. The method of claim 13, further comprising the plurality of vehicles restarting the copy of the vehicle application after updating the copy of the vehicle application based on the patch data.

15. The method of claim 11, wherein distributing the patch includes installing the patch in one or more of the vehicles included in the fleet of vehicles at a service station.

16. A non-transitory memory of a computer system, the non-transitory memory comprising computer code which, when executed by a processor, causes the processor to perform steps comprising:

receiving a set of trace data associated with a vehicle application from a plurality of vehicles included in a fleet of vehicles, wherein the plurality of vehicles each include a copy of the vehicle application and the set of trace data describes one or more operations that are executed responsive to an onboard vehicle computer executing the copy of the vehicle application;

inputting the set of trace data and model data to an RV-Predict application, wherein the model data describes a formal model of the vehicle application; and

executing the RV-Predict application with a processor to generate predictive data describing a predictive analysis of whether the vehicle application includes an error, wherein the predictive analysis is configured to never include a false positive.

17. The non-transitory memory of the computer system of claim 16, wherein the non-transitory memory includes further comprises computer code which, when executed by the processor, causes the processor to perform steps comprising:

determining, based on the predictive data, that the vehicle application includes the error;

generating a patch that is operable to be installed in the copy of the vehicle application to correct the error; and

distributing the patch to the plurality of vehicles included in the fleet of vehicles.

18. A system comprising:

a processor; and

a non-transitory memory storing an RV-Predict application, a set of trace data associated with a vehicle application and model data describing a formal model for the vehicle application;

wherein the set of trace data is received from a fleet of vehicles that include a copy of the vehicle application and the set of trace data describes one or more operations that are executed responsive to an onboard computer executing the copy of the vehicle application;

wherein the set of trace data and the model data are inputted to the RV-Predict application; and

wherein the RV-Predict application, responsive to being executed by the processor, generates predictive data describing a predictive analysis of whether the vehicle application includes an error.

19. The system of claim 18, wherein the predictive analysis is proactive and occurs before the error is identifiable by the operation of the fleet of vehicles.

20. The system of claim 18, wherein one or more batches of trace data are periodically received on a continuing basis and the predictive analysis is periodically repeated using the one or more batches of trace data.