Systems and methods for detecting software interactions for individual autonomous vehicles

• US 10,703,383 B1
• Filed: 04/05/2019
• Issued: 07/07/2020
• Est. Priority Date: 04/05/2019
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Chan, Aaron Scott, Cielocha, Steve, Fields, Brian
• Primary Examiner(s): Akki, Munear T
• Application Number:
  US16/376,818
• Time in Patent Office: 459 Days
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2556/05 Big data
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: B60W 50/045 Monitoring control system parameters
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C

Abstract

An interaction detection and analysis (“IDA”) computing device for analyzing data from a plurality of autonomous vehicles (“AVs”) relative to an individual AV that identifies potential performance outcomes and risks based on interactions associated with software onboard the AVs may be provided. The IDA computing device may include at least one processor programmed to (i) receive AV data from the individual AV including a software ecosystem and hardware installed on the individual AV, (ii) store the received AV data in a data record in a database, (iii) compare the software ecosystem and the hardware for the individual AV to data associated with AVs having relatively similar software ecosystems and hardware, (iv) identify at least one known performance outcome and risk based on the data from the AVs having relatively similar software ecosystems and hardware, and (v) alert a user of the individual AV of the identified performance outcome and risk.

Claims

1. An interaction detection and analysis (“

IDA”

) computing device for analyzing data from a plurality of autonomous vehicles (“

AVs”

) relative to an individual AV that identifies potential performance outcomes and risks based on interactions associated with software onboard the AVs, the computing device including at least one processor in communication with at least one memory device, the at least one processor is programmed to;

receive AV data from the individual AV, the AV data including software ecosystem data that represents a software ecosystem of the individual AV and hardware data that represents hardware installed on the individual AV;

store the received AV data in a data record in a database, wherein the data record is associated with the individual AV;

compare the software ecosystem data and the hardware data for the individual AV to data associated with AVs having similar software ecosystems and hardware to the individual AV;

identify at least one performance outcome and risk associated therewith based on the data from the AVs having similar software ecosystems and hardware to the individual AV; and

alert a user of the individual AV of the at least one identified performance outcome and associated risk.

2. The IDA computing device of claim 1, wherein the processor is further programmed to:

calculate a risk score based on the at least one identified performance outcome and risk; and

transmit the calculated risk score to a remote computing device.

3. The IDA computing device of claim 2, wherein the processor is further programmed to:

display, on a user interface of the individual AV, available software updates to the user of the individual AV.

4. The IDA computing device of claim 3, wherein the processor is further programmed to:

group the available software updates together depending on a system of the individual AV that the available software updates correspond to;

determine a priority score for each group of available software updates; and

display the available software updates to the user of the individual AV based on the priority scores for the groups.

5. The IDA computing device of claim 4, wherein the processor is further programmed to:

calculate an elapsed time between a date an update of a software application onboard the AV was made available to the user and a date the user updated the software application; and

calculate a compliance score for the user of the individual AV based on the calculated elapsed time.

6. The IDA computing device of claim 1, wherein the software ecosystem includes at least one of i) all software applications currently installed on the individual AV, ii) a current version of each installed software application, and iii) a date of installation of each installed software application, and iv) a developer of each installed software application.

7. The computing device of claim 1, wherein the hardware includes at least one of:

i) a serial number for sensor systems, graphic processing units (GPU), electronic control units (ECU), cameras, ii) a make, a model, and a year of manufacture of the individual AV, and iii) a manufacturer, a model, and date of installation for hardware components installed on the AV.

8. A computer-implemented method for analyzing data from a plurality of autonomous vehicles (“

AVs”

) relative to an individual AV that identifies potential performance outcomes and risks based on interactions associated with software onboard the AVs, the method implemented using an interaction and detection analysis (“

IDA”

) computing device including at least one processor in communication with at least one memory, the method comprising;

receiving, from a remote computing device, AV data including software ecosystem data that represents a software ecosystem of the individual AV and hardware data that represents hardware installed on the individual AV;

storing the received AV data in a data record in a database, wherein the data record is associated with the individual AV;

comparing the stored software ecosystem data and hardware data for the individual AV to data associated with AVs having similar software ecosystems and hardware to the individual AV;

identifying at least one performance outcome and risk associated therewith based on the data from the AVs having similar software ecosystems and hardware to the individual AV; and

alerting a user of the individual AV of the at least one identified performance outcome and associated risk.

9. The method of claim 8, further comprising:

calculating a risk score based on the at least one identified performance outcome and risk; and

transmitting the calculated risk score to a remote computing device.

10. The method of claim 9, further comprising:

displaying, on a user interface of the individual AV, available software updates to the user of the individual AV.

11. The method of claim 10, further comprising:

grouping the available software updates together depending on an operating system of the individual AV that the available software updates correspond to;

determining a priority score for each group of available software updates; and

displaying the available software updates to the user of the individual AV based on the priority scores for the groups.

12. The method of claim 11, further comprising:

calculating an elapsed time between a date an update of a software application onboard the individual AV was made available to the user and a date the user updated the software application; and

calculating a compliance score for the user of the individual AV based on the calculated elapsed time.

13. The method of claim 8, wherein the software ecosystem includes at least one of i) all software applications currently installed on the individual AV, ii) a current version of each installed software application, and iii) a date of installation of each installed software application, and iv) a developer of each installed software application.

14. The method of claim 8, wherein the hardware includes at least one of i) a serial number for sensor systems, graphic processing units (GPU), electronic control units (ECU), cameras, ii) a make, a model, and a year of manufacture of the individual AV, and iii) a manufacturer, a model, and date of installation for hardware components installed on the AV.

15. At least one non-transitory computer-readable media having computer-executable instructions thereon, wherein when executed by an interaction detection and analysis (“

IDA”

) computing device of an autonomous vehicle (“

AV”

), cause the at least one processor of the computing device to;

receive, from a remote computing device, AV data including software ecosystem data that represents a software ecosystem of an individual AV and hardware ecosystem data that represents hardware installed on the individual AV;

store the received AV data in a data record in a database, wherein the data record is associated with the individual AV;

compare the stored software ecosystem data and hardware data for the individual AV to data associated with AVs having similar software ecosystems and hardware to the individual AV;

identify at least one performance outcome and risk associated therewith based on the data from the AVs having similar software ecosystems and hardware to the individual AV; and

alert a user of the individual AV of the at least one identified performance outcome and associated risk.

16. The computer-readable media of claim 15, wherein the computer-executable instructions further cause the at least one processor to:

calculate a risk score based on the at least one identified performance outcome and risk; and

transmitting the calculated risk score to a remote computing device.

17. The computer-readable media of claim 16, wherein the computer-executable instructions further cause the at least one processor to:

display, on a user interface of the individual AV, available software updates to the user of the individual AV.

18. The computer-readable media of claim 17, wherein the computer-executable instructions further cause the at least one processor to:

group the available software updates together depending on an operating system of the individual AV that the available software updates update;

determine a priority score for each group of available software updates; and

display the available software updates to the user of the individual AV based on the priority scores for the groups.

19. The computer-readable media of claim 18, wherein the computer-executable instructions further cause the at least one processor to:

calculate an elapsed time between a date an update of a software application onboard the individual AV was made available to the user and a date the user updated the software application; and

calculate a compliance score for the user of the individual AV based on the calculated elapsed time.

20. The computer-readable media of claim 15, wherein the software ecosystem includes at least one of i) all software applications currently installed on the individual AV, ii) a current version of each installed software application, and iii) a date of installation of each installed software application, and iv) a developer of each installed software application.