Safety controls for network connected autonomous vehicle

• US 10,289,114 B2
• Filed: 08/08/2017
• Issued: 05/14/2019
• Est. Priority Date: 08/08/2017
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Mitchell, Timothy M., Shelton, Jason W., Lawson, Jack D.
• Primary Examiner(s): Nguyen, Tan Q
• Application Number:
  US15/671,820
• Publication Number:
  US 20190049954A1
• Time in Patent Office: 644 Days
• Field of Search: 701 23
• CPC Class Codes: B60W 2050/046 involving external transmission of data to or from the vehicle, e.g. via telemetry, satellite, Global Positioning System [GPS]
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2540/26 Incapacity
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2756/10 Involving external transmission of data to or from the vehicle
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/04 Monitoring the functioning of the control system
• CPC Class Codes: B60W 60/0018 by employing degraded modes, e.g. reducing speed, in response to suboptimal conditions
• CPC Class Codes: B60W 60/00182 in response to weather conditions
• CPC Class Codes: B60W 60/00186 related to the vehicle
• CPC Class Codes: B60W 60/0053 from vehicle to occupant
• CPC Class Codes: B60W 60/0059 Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G05D 1/0061 for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0297 by controlling means in a control room
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04W 12/12 Detection or prevention of fraud
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

Method and apparatus for ensuring safety controls for a network-connected autonomous vehicle. The method and apparatus monitor a respective state of each of one or more data communication connections one or more data communication networks. One of a plurality of operational modes for the autonomous vehicle is selected based on the monitored states. Each of the plurality of operational modes defines a respective level of autonomous control for the autonomous vehicle. The method and apparatus transition operation of the autonomous vehicle to the selected operational mode.

Claims

1. A method of ensuring safety controls for a network-connected autonomous vehicle, comprising:

monitoring data collected for one or more network performance metrics for one or more data communication connections across one or more data communication networks, to determine a respective state of the one or more data communication connections;

selecting one of a plurality of operational modes for the autonomous vehicle based on the monitored states, wherein each of the plurality of operational modes defines a respective level of autonomous control for the autonomous vehicle; and

transitioning operation of the autonomous vehicle to the selected operational mode.

2. The method of claim 1, further comprising:

determining real-time weather conditions relating to operation of the autonomous vehicle,

wherein selecting one of the plurality of operational modes for the autonomous vehicle is further based on the determined real-time weather conditions.

3. The method of claim 1, further comprising:

determining real-time road conditions relating to operation of the autonomous vehicle,

wherein selecting one of the plurality of operational modes for the autonomous vehicle is further based on the determined real-time road conditions.

4. The method of claim 1, further comprising:

collecting real-time data, using one or more sensor devices of the autonomous vehicle, describing one or more attributes of a second vehicle that is proximate to the autonomous vehicle; and

transmitting the collected real-time data to a remote system for use in managing an operation of one or more autonomous vehicles.

5. The method of claim 1, further comprising:

receiving, from a remote system, real-time data collected using one or more sensor devices of one or more autonomous vehicles, the real-time data describing one or more attributes of a manually controlled vehicle that is proximate to the autonomous vehicle,

wherein selecting one of the plurality of operational modes for the autonomous vehicle is further based on the received real-time data.

6. The method of claim 1, further comprising:

determining a measure of driving skill of an operator of the autonomous vehicle,

wherein selecting one of the plurality of operational modes for the autonomous vehicle is further based on the measure of driving skill.

7. The method of claim 1, further comprising:

determining an operational mode of one or more autonomous vehicles operating in proximity to the autonomous vehicle,

wherein selecting one of the plurality of operational modes for the autonomous vehicle is further based on the operational mode of the one or more autonomous vehicles.

8. The method of claim 1, wherein the selected operational mode for the autonomous vehicle further comprises at least one of (i) a fully autonomous operational mode, (ii) a partially autonomous operational mode, and (iii) a fully manual operational mode, wherein the partially autonomous mode further comprises disabling one or more entertainment systems within the autonomous vehicle, while continuing to operate the autonomous vehicle in a computer controlled manner.

9. The method of claim 1, further comprising:

determining a level of driver skill of an operator of the autonomous vehicle; and

updating a data model based on the level of driver skill, wherein the data model is used in selecting the operational mode for the autonomous vehicle.

10. The method of claim 9, wherein determining the level of driver skill of the operator of the autonomous vehicle further comprises:

instructing the operator of the autonomous vehicle to perform one or more driving operations,

wherein determining the level of driver skill is based at least in part on the operator'"'"'s performance of the one or more driving operations.

11. The method of claim 9, wherein determining the level of driver skill of the operator of the autonomous vehicle further comprises:

retrieving a driver history for the operator of the autonomous vehicle,

wherein determining the level of driver skill is based at least in part on the retrieved driver history.

12. The method of claim 9, wherein determining the level of driver skill of the operator of the autonomous vehicle further comprises:

monitoring driving behavior of the operator of the autonomous vehicle, while the autonomous vehicle is in a manual control operational mode; and

assessing the driving behavior of the operator of the autonomous vehicle, by comparing the monitored driving behavior with a determined optimal driving behavior for the autonomous vehicle,

wherein determining the level of driver skill is based at least in part on the assessment of the operator'"'"'s driving behavior.

13. The method of claim 1, further comprising:

transmitting a notification over a data communications network, notifying a remote system of the transition to the selected operational mode for the autonomous vehicle.

14. The method of claim 1, wherein the selected operational mode for the autonomous vehicle further comprises at least one of (i) a full authority autonomous operational mode, (ii) a supervisory operational mode, (iii) an alert operator operational mode, (iv) an aware operator operational mode, (v) a conscious operator operational mode, (vi) a sleeping operator operational mode, and (vii) a no operator operational mode.

15. The method of claim 1, further comprising:

providing an indication of the selected operational mode to vehicles in proximity of the autonomous vehicle.

16. A system, comprising:

one or more computer processors; and

a memory containing computer program code that, when executed by operation of the one or more computer processors, performs an operation for ensuring safety controls for a network-connected autonomous vehicle, the operation comprising;

monitoring data collected for one or more network performance metrics for one or more data communication connections across one or more data communication networks, to determine a respective state of the one or more data communication connections;

selecting one of a plurality of operational modes for the autonomous vehicle based on the monitored states, wherein each of the plurality of operational modes defines a respective level of autonomous control for the autonomous vehicle; and

transitioning operation of the autonomous vehicle to the selected operational mode.

17. The system of claim 16, the operation further comprising:

determining real-time weather conditions relating to operation of the autonomous vehicle;

determining real-time road conditions relating to operation of the autonomous vehicle;

determining a measure of driving skill of an operator of the autonomous vehicle; and

determining an operational mode of one or more autonomous vehicles operating in proximity to the autonomous vehicle,

wherein selecting one of the plurality of operational modes for the autonomous vehicle is further based on the real-time weather conditions, the real-time road conditions, the measure of driving skill of the operator of the autonomous vehicle, and the operational mode of the one or more autonomous vehicles.

18. The system of claim 16, the operation further comprising:

determining a level of driver skill of an operator of the autonomous vehicle; and

updating a data model based on the level of driver skill, wherein the data model is used in selecting the operational mode for the autonomous vehicle.

19. The system of claim 16, wherein the selected operational mode for the autonomous vehicle further comprises at least one of (i) a full authority autonomous operational mode, (ii) a supervisory operational mode, (iii) an alert operator operational mode, (iv) an aware operator operational mode, (v) a conscious operator operational mode, (vi) a sleeping operator operational mode, and (vii) a no operator operational mode.

20. A non-transitory computer-readable medium containing computer program code that, when executed by operation of one or more computer processors, performs an operation for ensuring safety controls for a network-connected autonomous vehicle, the operation comprising:

monitoring data collected for one or more network performance metrics for one or more data communication connections across one or more data communication networks, to determine a respective state of the one or more data communication connections;

selecting one of a plurality of operational modes for the autonomous vehicle based on the monitored states, wherein each of the plurality of operational modes defines a respective level of autonomous control for the autonomous vehicle; and

transitioning operation of the autonomous vehicle to the selected operational mode.