Controlling driving modes of self-driving vehicles

• US 9,785,145 B2
• Filed: 08/07/2015
• Issued: 10/10/2017
• Est. Priority Date: 08/07/2015
• Status: Active Grant

• Current Assignee: Maplebear Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Gordon, Michael S., Kozloski, James R., Kundu, Ashish, Malkin, Peter K., Pickover, Clifford A.
• Primary Examiner(s): Kiswanto, Nicholas
• Assistant Examiner(s): TAVERAS, KENNY ABEL
• Application Number:
  US14/820,620
• Publication Number:
  US 20170038773A1
• Time in Patent Office: 795 Days
• Field of Search: None
• CPC Class Codes: B60W 2050/0073 Driver overrides controller
• CPC Class Codes: B60W 2050/0295 Inhibiting action of specific actuators or systems
• CPC Class Codes: B60W 2540/00 Input parameters relating to occupants
• CPC Class Codes: B60W 2540/215 Selection or confirmation of options
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 50/08 Interaction between the driver and the control system
• CPC Class Codes: B60W 50/12 Limiting control by the driver depending on vehicle state, e.g. interlocking means for the control input for preventing unsafe operation
• 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: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G08G 1/0112 : from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 : from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 : for classifying traffic situation
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 : where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station

Abstract

A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). Sensor readings describe a current condition of a roadway, which is part of a planned route of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of the on-board SDV control processor that autonomously controls the SDV to a human driver competence level of a human driver in controlling the SDV under the current condition of the roadway. One or more processors then selectively assign control of the SDV to the on-board SDV control processor or to the human driver based on which of the control processor competence level and the human driver competence level is relatively higher to the other.

Claims

1. A computer-implemented method for controlling a driving mode of a self-driving vehicle (SDV), the computer-implemented method comprising:

receiving, by one or more processors, sensor readings from a sensor, wherein the sensor readings describe a current condition of a roadway, wherein the roadway is part of a planned route of a self-driving vehicle (SDV), wherein the SDV is capable of being operated in autonomous mode by an on-board SDV control processor, wherein a driving mode module selectively controls whether the SDV is operated in the autonomous mode or in manual mode, and wherein the SDV is controlled by a human driver of the SDV if in the manual mode;

determining, by one or more processors, a control processor competence level of the on-board SDV control processor, wherein the control processor competence level describes a competence level of the on-board SDV control processor in controlling the SDV under the current condition of the roadway;

receiving, by one or more processors, a driver profile of the human driver of the SDV, wherein the driver profile describes a human driver competence level of the human driver in controlling the SDV under the current condition of the roadway;

comparing, by one or more processors, the control processor competence level to the human driver competence level; and

selectively assigning, by one or more processors, control of the SDV to the on-board SDV control processor or to the human driver based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

2. The processor-implemented method of claim 1, further comprising:

receiving, by one or more processors, a manual input, wherein the manual input describes the current condition of the roadway, and wherein the manual input overrides sensor readings that describe the current condition of the roadway;

defining, by one or more processors, an updated current condition of the roadway based on the manual input;

re-determining, by one or more processors, the control processor competence level of the on-board SDV control processor based on the updated current condition of the roadway from the manual input to create a re-determined control processor competence level;

redefining, by one or more processors, the driver profile of the human driver of the SDV based on the updated current condition of the roadway from the manual input to create a redefined human driver competence level;

comparing, by one or more processors, the re-determined control processor competence level to the redefined human driver competence level; and

selectively assigning, by one or more processors, control of the SDV to the on-board SDV control processor or the human driver based on which of the re-determined control processor competence level and the redefined human driver competence level is relatively higher to one another.

3. The processor-implemented method of claim 1, wherein the sensor is mounted on the SDV, wherein the sensor readings describe environmental conditions of the SDV in real time, and wherein the processor-implemented method further comprises:

receiving, by one or more processors, an environmental report from an environmental reporting service, wherein the environmental report describes a general condition for the roadway;

comparing, by one or more processors, environmental information from the environmental report to the sensor readings that describe the environmental conditions of the SDV in real time; and

in response to the environmental report disagreeing with the sensor readings, disregarding, by one or more processors, the sensor readings from the sensor and using the environmental report to describe the current condition of the roadway.

4. The processor-implemented method of claim 1,

retrieving, by one or more processors, driver profile information about the human driver of the SDV;

assigning, by one or more processors, the human driver of the SDV to a cohort of drivers traveling on the roadway in multiple SDVs, wherein the human driver of the SDV shares more than a predetermined quantity of traits with members of the cohort of drivers;

retrieving, by one or more processors, traffic pattern data for the multiple SDVs occupied by the cohort of drivers traveling on the roadway;

examining, by one or more processors, the traffic pattern data to determine a first traffic flow of the multiple SDVs occupied by members of the cohort of drivers, wherein the multiple SDVs in the first traffic flow are operating in the autonomous mode on the roadway;

examining, by one or more processors, the traffic pattern data to determine a second traffic flow of the multiple SDVs occupied by members of the cohort of drivers, wherein the multiple SDVs in the second traffic flow are operating in the manual mode on the roadway; and

in response to determining that the first traffic flow has a lower accident rate than the second traffic flow, prohibiting, by one or more processors, the SDV from operating in the manual mode.

5. The processor-implemented method of claim 1, further comprising:

receiving, by one or more processors, sensor readings from multiple sensors, wherein each of the multiple sensors detects a different type of current condition of the roadway;

weighting, by one or more processors, each of the sensor readings for different current conditions of the roadway;

summing, by one or more processors, weighted sensor readings for the different current conditions of the roadway;

determining, by one or more processors, whether the summed weighted sensor readings exceed a predefined level; and

in response to determining that the summed weighted sensor readings exceed a predefined level, prohibiting, by the on-board SDV control processor, the SDV from operating in the manual mode.

6. The processor-implemented method of claim 1, further comprising:

receiving, by one or more processors, operational readings from one or more operational sensors on the SDV, wherein the operational sensors detect a current state of mechanical equipment on the SDV;

detecting, by the one or more processors and based on received operational readings, a mechanical fault with the mechanical equipment on the SDV; and

in response to detecting the mechanical fault with the mechanical equipment on the SDV, prohibiting, by the on-board SDV control processor, the SDV from operating in the manual mode.

7. The processor-implemented method of claim 1, further comprising:

setting, by one or more processors, a minimum competence level threshold for the control processor competence level and the human driver competence level;

determining, by one or more processors, that neither the control processor competence level nor the human driver competence level exceeds the minimum competence level threshold; and

in response to determining that neither the control processor competence level nor the human driver competence level exceeds the minimum competence level threshold, directing, by the driving mode module, the on-board SDV control processor to take control of the SDV and to bring the SDV to a stop.

8. A computer program product for controlling a driving mode of a self-driving vehicle (SDV), the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code readable and executable by a processor to perform a method comprising:

receiving sensor readings from a sensor, wherein the sensor readings describe a current condition of a roadway, wherein the roadway is part of a planned route of a self-driving vehicle (SDV), wherein the SDV is capable of being operated in autonomous mode by an on-board SDV control processor, wherein a driving mode module selectively controls whether the SDV is operated in the autonomous mode or in manual mode, and wherein the SDV is controlled by a human driver of the SDV if in the manual mode;

determining a control processor competence level of the on-board SDV control processor, wherein the control processor competence level describes a competence level of the on-board SDV control processor in controlling the SDV under the current condition of the roadway;

receiving a driver profile of the human driver of the SDV, wherein the driver profile describes a human driver competence level of the human driver in controlling the SDV under the current condition of the roadway;

comparing the control processor competence level to the human driver competence level; and

selectively assigning control of the SDV to the on-board SDV control processor or to the human driver based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

9. The computer program product of claim 8, wherein the method further comprises:

receiving a manual input, wherein the manual input describes the current condition of the roadway, and wherein the manual input overrides sensor readings that describe the current condition of the roadway;

defining an updated current condition of the roadway based on the manual input;

re-determining the control processor competence level of the on-board SDV control processor based on the updated current condition of the roadway from the manual input to create a re-determined control processor competence level;

redefining the driver profile of the human driver of the SDV based on the updated current condition of the roadway from the manual input to create a redefined human driver competence level;

comparing the re-determined control processor competence level to the redefined human driver competence level; and

selectively assigning control of the SDV to the on-board SDV control processor or the human driver based on which of the re-determined control processor competence level and the redefined human driver competence level is relatively higher to one another.

10. The computer program product of claim 8, wherein the sensor is mounted on the SDV, wherein the sensor readings describe environmental conditions of the SDV in real time, and wherein the method further comprises:

receiving an environmental report from an environmental reporting service, wherein the environmental report describes a general condition for the roadway;

comparing environmental information from the environmental report to the sensor readings that describe the environmental conditions of the SDV in real time; and

in response to the environmental report disagreeing with the sensor readings, disregarding the sensor readings from the sensor and using the environmental report to describe the current condition of the roadway.

11. The computer program product of claim 8, wherein the method further comprises:

retrieving driver profile information about the human driver of the SDV;

assigning the human driver of the SDV to a cohort of drivers traveling on the roadway in multiple SDVs, wherein the human driver of the SDV shares more than a predetermined quantity of traits with members of the cohort of drivers;

retrieving traffic pattern data for the multiple SDVs occupied by the cohort of drivers traveling on the roadway;

examining the traffic pattern data to determine a first traffic flow of the multiple SDVs occupied by members of the cohort of drivers, wherein the multiple SDVs in the first traffic flow are operating in the autonomous mode on the roadway;

examining the traffic pattern data to determine a second traffic flow of the multiple SDVs occupied by members of the cohort of drivers, wherein the multiple SDVs in the second traffic flow are operating in the manual mode on the roadway; and

in response to determining that the first traffic flow has a lower accident rate than the second traffic flow, prohibiting the SDV from operating in the manual mode.

12. The computer program product of claim 8, wherein the method further comprises:

receiving sensor readings from multiple sensors, wherein each of the multiple sensors detects a different type of current condition of the roadway;

weighting each of the sensor readings for different current conditions of the roadway;

summing weighted sensor readings for the different current conditions of the roadway;

determining whether the summed weighted sensor readings exceed a predefined level; and

in response to determining that the summed weighted sensor readings do not exceed a predefined level, prohibiting, via the on-board SDV control processor, the SDV from operating in the autonomous mode.

13. The computer program product of claim 8, wherein the method further comprises:

receiving operational readings from one or more operational sensors on the SDV, wherein the operational sensors detect a current state of mechanical equipment on the SDV;

detecting, based on received operational readings, a mechanical fault with the mechanical equipment on the SDV; and

in response to detecting the mechanical fault with the mechanical equipment on the SDV, prohibiting, via the on-board SDV control processor, the SDV from operating in the manual mode.

14. The computer program product of claim 8, wherein the method further comprises:

setting a minimum competence level threshold for the control processor competence level and the human driver competence level;

determining that neither the control processor competence level nor the human driver competence level exceeds the minimum competence level threshold; and

in response to determining that neither the control processor competence level nor the human driver competence level exceeds the minimum competence level threshold, directing the on-board SDV control processor to take control of the SDV and to bring the SDV to a stop.

15. A computer system comprising:

a processor, a computer readable memory, and a non-transitory computer readable storage medium;

first program instructions to receive sensor readings from a sensor, wherein the sensor readings describe a current condition of a roadway, wherein the roadway is part of a planned route of a self-driving vehicle (SDV), wherein the SDV is capable of being operated in autonomous mode by an on-board SDV control processor, wherein a driving mode module selectively controls whether the SDV is operated in the autonomous mode or in manual mode, and wherein the SDV is controlled by a human driver of the SDV if in the manual mode;

second program instructions to determine a control processor competence level of the on-board SDV control processor, wherein the control processor competence level describes a competence level of the on-board SDV control processor in controlling the SDV under the current condition of the roadway;

third program instructions to receive a driver profile of the human driver of the SDV, wherein the driver profile describes a human driver competence level of the human driver in controlling the SDV under the current condition of the roadway;

fourth program instructions to compare the control processor competence level to the human driver competence level; and

fifth program instructions to selectively assign control of the SDV to the on-board SDV control processor or to the human driver based on which of the control processor competence level and the human driver competence level is relatively higher to one another; and

wherein the first, second, third, fourth, and fifth program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory.

16. The computer system of claim 15, further comprising:

sixth program instructions to receive a manual input, wherein the manual input describes the current condition of the roadway, and wherein the manual input overrides sensor readings that describe the current condition of the roadway;

seventh program instructions to define an updated current condition of the roadway based on the manual input;

eighth program instructions to re-determine the control processor competence level of the on-board SDV control processor based on the updated current condition of the roadway from the manual input to create a re-determined control processor competence level;

ninth program instructions to redefine the driver profile of the human driver of the SDV based on the updated current condition of the roadway from the manual input to create a redefined human driver competence level;

tenth program instructions to compare the re-determined control processor competence level to the redefined human driver competence level; and

eleventh program instructions to selectively assign control of the SDV to the on-board SDV control processor or the human driver based on which of the re-determined control processor competence level and the redefined human driver competence level is relatively higher to one another; and

wherein

the sixth, seventh, eighth, ninth, tenth, and eleventh program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory.

17. The computer system of claim 15, wherein the sensor is mounted on the SDV, wherein the sensor readings describe environmental conditions of the SDV in real time, and wherein the computer system further comprises:

sixth program instructions to receive an environmental report from an environmental reporting service, wherein the environmental report describes a general condition for the roadway;

seventh program instructions to compare environmental information from the environmental report to the sensor readings that describe the environmental conditions of the SDV in real time; and

eighth program instructions to, in response to the environmental report disagreeing with the sensor readings, disregard the sensor readings from the sensor and using the environmental report to describe the current condition of the roadway; and

wherein the sixth, seventh, and eighth program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory.

18. The computer system of claim 15, further comprising:

sixth program instructions to retrieve driver profile information about the human driver of the SDV;

seventh program instructions to assign the human driver of the SDV to a cohort of drivers traveling on the roadway in multiple SDVs, wherein the human driver of the SDV shares more than a predetermined quantity of traits with members of the cohort of drivers;

eighth program instructions to retrieve traffic pattern data for the multiple SDVs occupied by the cohort of drivers traveling on the roadway;

ninth program instructions to examine the traffic pattern data to determine a first traffic flow of the multiple SDVs occupied by members of the cohort of drivers, wherein the multiple SDVs in the first traffic flow are operating in the autonomous mode on the roadway;

tenth program instructions to examine the traffic pattern data to determine a second traffic flow of the multiple SDVs occupied by members of the cohort of drivers, wherein the multiple SDVs in the second traffic flow are operating in the manual mode on the roadway; and

eleventh program instructions to, in response to determining that the first traffic flow has a lower accident rate than the second traffic flow, prohibit the SDV from operating in the manual mode; and

wherein

the sixth, seventh, eighth, ninth, tenth, and eleventh program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory.

19. The computer system of claim 15, further comprising:

sixth program instructions to receive sensor readings from multiple sensors, wherein each of the multiple sensors detects a different type of current condition of the roadway;

seventh program instructions to weight each of the sensor readings for different current conditions of the roadway;

eighth program instructions to sum weighted sensor readings for the different current conditions of the roadway;

ninth program instructions to determine whether the summed weighted sensor readings exceed a predefined level; and

tenth program instructions to, in response to determining that the summed weighted sensor readings do not exceed a predefined level, prohibit, via the on-board SDV control processor, the SDV from operating in the autonomous mode; and

wherein

the sixth, seventh, eighth, ninth, and tenth program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory.

20. The computer system of claim 15, further comprising:

sixth program instructions to receive operational readings from one or more operational sensors on the SDV, wherein the operational sensors detect a current state of mechanical equipment on the SDV;

seventh program instructions to detect, based on received operational readings, a mechanical fault with the mechanical equipment on the SDV; and

eighth program instructions to, in response to detecting the mechanical fault with the mechanical equipment on the SDV, prohibit, via the on-board SDV control processor, the SDV from operating in the manual mode; and

wherein

the sixth, seventh, and eighth program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory.