MANUAL VEHICLE CONTROL NOTIFICATION

• US 20170066452A1
• Filed: 09/04/2015
• Published: 03/09/2017
• Est. Priority Date: 09/04/2015
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Scofield, Christopher L.
• Granted Patent: US 11,040,725 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60K 28/00 Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions for electrically-propelled vehicles B60L3/00; road vehicle drive control systems for purposes not related to the control of a particular sub-unit B60W30/00; drive control systems specially adapted for autonomous road vehicles B60W60/00
• CPC Class Codes: B60W 2040/0872 Driver physiology
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• 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 50/082 Selecting or switching between different modes of propelling
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• 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: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees

Abstract

One or more techniques and/or systems are provided for notifying drivers to assume manual vehicle control of vehicles. For example, sensor data is acquired from on-board vehicles sensors (e.g., radar, sonar, and/or camera imagery of a crosswalk) of a vehicle that is in an autonomous driving mode. In an example, the sensor data is augmented with driving condition data aggregated from vehicle sensor data of other vehicles (e.g., a cloud service collects and aggregates vehicle sensor data from vehicles within the crosswalk to identify and provide the driving condition data to the vehicle). The sensor data (e.g., augmented sensor data) is evaluated to identify a driving condition of a road segment, such as the crosswalk (e.g., pedestrians protesting within the crosswalk). Responsive to the driving condition exceeding a complexity threshold for autonomous driving decision making functionality, a driver alert to assume manual vehicle control may be provided to a driver.

Claims

1. A method for notifying a driver to assume manual vehicle control, comprising:

acquiring sensor data from one or more on-board vehicle sensors of a vehicle that is in an autonomous driving mode;

evaluating the sensor data to determine a driving condition of a road segment that is to be traveled by the vehicle;

responsive to determining that the driving condition exceeds a complexity threshold for autonomous driving decision making functionality, generating a driver alert to assume manual vehicle control; and

providing the driver alert to a driver of the vehicle at a point in time, relative to encountering the road segment, based upon the driving condition.

2. The method of claim 1, comprising:

determining a cognitive load of the driver using at least one of a sensor or an evaluation of user data; and

determining whether to provide the driver alert to the driver based upon the cognitive load.

3. The method of claim 2, comprising:

determining the point in time for providing the driver alert based upon the cognitive load.

4. The method of claim 2, the determining a cognitive load comprising:

determining the cognitive load based upon at least one of a current activity of the driver, an awareness of the driver, a driving expertise level of the driver, or a mental state of the driver.

5. The method of claim 1, comprising:

sending the sensor data to a remote computing device for processing; and

receiving, from the remote computing device based upon the remote computing device processing the sensor data, at least one of driving condition data or an instruction as to whether to provide the driver alert.

6. The method of claim 1, comprising:

determining the point in time for providing the driver alert based upon at least one of a potential alternate route or a state of traffic.

7. The method of claim 1, comprising:

utilizing travel prediction functionality to predict a route of the vehicle; and

identifying the road segment based upon the route and a current location of the vehicle.

8. The method of claim 1, comprising:

receiving driving condition data for the road segment from a remote computing device over a communication connection; and

augmenting the sensor data with the driving condition data.

9. The method of claim 8, the driving condition data corresponding to weather data, event data, traffic accident data, vehicle sensor data, driving impediment data, or data from a service.

10. The method of claim 8, the driving condition data corresponding to an aggregation of vehicle sensor data from one or more vehicles while the one or more vehicles traveled the road segment.

11. The method of claim 1, comprising:

receiving, from a remote computing device over a communication connection, a second driving condition for a second road segment that is to be traveled by the vehicle;

responsive to determining that the second driving condition exceeds the complexity threshold for autonomous driving decision making functionality, generating a second driver alert to assume manual vehicle control; and

providing the second driver alert to the driver of the vehicle at a second point in time, relative to the second road segment, based upon the second driving condition.

12. The method of claim 1, the driving condition corresponding to at least one of a road condition, a weather condition, a pedestrian, an accident, an ethical decision, or a driving impediment.

13. A system for notifying a driver to assume manual vehicle control, comprising:

a driving condition aggregator configured to;

identify a current location of a vehicle that is in an autonomous driving mode;

determine a route of the vehicle;

acquire vehicle sensor data from one or more vehicles traveling a road segment of the route, the road segment not yet traveled by the vehicle;

aggregate the vehicle sensor data to determine driving condition data for the road segment; and

providing an instruction, comprising the driving condition data, to the vehicle, the instruction specifying that the vehicle is to determine whether to provide a driver of the vehicle with a driver alert to assume manual vehicle control of the vehicle based upon the driving condition data.

14. The system of claim 13, the driving condition aggregator configured to:

determine the route as a predicted route of the vehicle based upon one or more prior trips of the vehicle.

15. A system for notifying a driver to assume manual vehicle control, comprising:

a driver alert component configured to;

acquire sensor data from one or more on-board vehicle sensors of a vehicle that is in an autonomous driving mode;

evaluate the sensor data to determine a driving condition of a road segment that is to be traveled by the vehicle;

responsive to determining that the driving condition exceeds a complexity threshold for autonomous driving decision making functionality, generate a driver alert to assume manual vehicle control; and

provide the driver alert to a driver of the vehicle at a point in time, relative to encountering the road segment, based upon the driving condition.

16. The system of claim 15, the driver alert component configured to:

receive driving condition data for the road segment from a remote computing device over a communication connection; and

augment the sensor data with the driving condition data.

17. The system of claim 15, the driver alert component configured to:

receive, from a remote computing device over a communication connection, a second driving condition for a second road segment that is to be traveled by the vehicle;

responsive to determining that the second driving condition exceeds the complexity threshold for autonomous driving decision making functionality, generate a second driver alert to assume manual vehicle control; and

provide the second driver alert to the driver of the vehicle at a second point in time, relative to the second road segment, based upon the second driving condition.

18. The system of claim 15, the driver alert component configured to:

utilize travel prediction functionality to predict a route of the vehicle; and

identify the road segment based upon the route and a current location of the vehicle.

19. The system of claim 15, the driver alert component configured to:

determine a cognitive load of the driver using at least one of a sensor or an evaluation of user data; and

determine whether to provide the driver alert to the driver based upon the cognitive load.

20. The system of claim 15, the driver alert component configured to at least one of:

determine the point in time for providing the driver alert based upon the cognitive load.