Intelligent vehicle action decisions

• US 10,703,381 B2
• Filed: 11/28/2018
• Issued: 07/07/2020
• Est. Priority Date: 11/28/2018
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bender, Michael, Boss, Gregory J., Avetisian, Albert, Fox, Jeremy R.
• Primary Examiner(s): Lee, Tyler J
• Application Number:
  US16/203,274
• Publication Number:
  US 20200164891A1
• Time in Patent Office: 587 Days
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2050/0083 Setting, resetting, calibration
• CPC Class Codes: B60W 2400/00 Indexing codes relating to detected, measured or calculated conditions or factors
• CPC Class Codes: B60W 2540/043 Identity of occupants
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2900/00 Indexing codes relating to the purpose of, or problem solved of road vehicle drive control systems not otherwise provided for in groups B60W30/00
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 50/0098 Details of control systems ensuring comfort, safety or stability not otherwise provided for
• CPC Class Codes: B60W 50/08 Interaction between the driver and the control system
• CPC Class Codes: B60W 50/087 where the control system corrects or modifies a request from the driver
• 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 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
• CPC Class Codes: G07C 5/02 Registering or indicating driving, working, idle, or waiting time only apparatus forming part of taximeters G07B13/00
• CPC Class Codes: G07C 5/085 using electronic data carriers

Abstract

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining sensor data from sensors disposed to output data indicative of vehicle driver performance of a first vehicle, the first vehicle having for a first trip an associated first driver user who drives the first vehicle during the first trip; processing the sensor data to return error event flags that indicate errors of the first driver user when driving the first vehicle during the first trip; recording for respective event flags of the error event flags values of a first driving environment classification parameter and values of a second driving environment classification parameter; and predicting a driving performance of the first driver user for a second trip, the second trip being a proposed trip to occur after the first trip.

Claims

1. A computer implemented method comprising:

obtaining sensor data from sensors disposed to output data indicative of vehicle driver performance of a first vehicle, the first vehicle having for a first trip an associated first driver user who drives the first vehicle during the first trip;

processing the sensor data to return error event flags that indicate errors of the first driver user when driving the first vehicle during the first trip;

recording for respective event flags of the error event flags values of a first driving environment classification parameter and values of a second driving environment classification parameter;

predicting a driving performance of the first driver user for a second trip, the second trip being a proposed trip to occur after the first trip, the predicting being in dependence on the error event flags, the values of a first driving environment classification parameter and the values of a second driving environment classification parameter, wherein the error event flags, the values of a first driving environment classification parameter and the values of a second driving environment classification parameter define historical driving performance data of the first driver user, wherein the second trip is a proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes obtaining candidate alternate routes for the second trip, and predicting safety performance of the first driver user for alternate routes of the candidate alternate routes; and

returning an action decision in dependence on the predicting, wherein the action decision is an action decision to recommend one or more of the alternate routes based on predicted safety performance of the first driver user as determined by the predicting safety performance.

2. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the predicting indicates that the first driver user is not qualified to drive the motor vehicle according one or more criterion and wherein the action decision includes an action decision to disable the motor vehicle so that the motor vehicle cannot be driven by the first driver user.

3. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the action decision includes an action decision to limit performance of the motor vehicle so that performance of the motor vehicle is modified when driven by the first driver user.

4. The computer implemented method of claim 1, wherein performing the predicting includes using a predictive model that has been trained by machine learning processes including application of iteratively applied sets of training data, wherein respective applied sets of training data for respective iterations of the iteratively applied set of training data include (a) an environment condition values dataset specifying environmental conditions for the first vehicle for a time segment of the first trip;

and (b) an error event flag dataset specifying error events of the first driver user during the time segment of the first trip.

5. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes detecting a presence of one or more passenger within the motor vehicle, further predicting the performance of the one or more passenger in driving the motor vehicle during the proposed trip, and wherein the returned action decision specifies sending a text based notification to the first driver user and the one or more passenger specifying a predicted performance of the one or more passenger in driving the motor vehicle during the proposed trip.

6. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes detecting a presence of one or more passenger within the motor vehicle, further predicting the performance of the one or more passenger in driving the motor vehicle during the proposed trip, and wherein the returned action decision specifies sending a text based notification to the first driver user and the one or more passenger specifying a predicted performance of the one or more passenger in driving the motor vehicle during the proposed trip, wherein the motor vehicle is provided by one of the following selected from the group consisting of:

(a) the first vehicle, and (b) a second vehicle different from the first vehicle.

7. The computer implemented method of claim 1, wherein the method includes tagging the event flags of the error event flags with values of the first driving environment classification parameter and with values of the second driving environment classification parameter.

8. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the predicting includes using a known vehicle type of the motor vehicle, forecasted roadway types of the second trip, forecasted times of day of the second trip, forecasted weather conditions for the second trip, and a forecasted duration of the second trip, wherein the predicting further includes using (i) historical driving performance data of the first driver user when driving vehicles of the type of the known vehicle type, (ii) historical driving performance data of the first driver user when driving roadways of the forecasted roadway types, (iii) historical driving performance data of the first driver user when driving during the forecasted times of day, (iv) historical driving performance data of the first driver user when driving forecasted weather condition;

and (v) historical driving performance data of the first driver user when driving on past trips of the forecasted duration.

9. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes detecting a presence of one or more passenger within the motor vehicle, further predicting the performance of the one or more passenger in driving the motor vehicle during the proposed trip, and wherein the action decision includes an action decision to prompt a switch a current driver of the motor vehicle for the second trip to a next driver provided by the one or more passenger.

10. The computer implemented method of claim 1, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes detecting a presence of one or more passenger within the motor vehicle, and wherein the action decision includes an action decision to send a notification to the one or more passenger within the motor vehicle specifying a predicted performance of the first driver user for the second trip.

11. The computer implemented method of claim 1, wherein the second trip is a proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes identifying a plurality of passengers located in the motor vehicle and wherein the method includes obtaining candidate alternate routes for the second trip, and predicting safety performance of the first driver user and the plurality of passengers for alternate routes of the candidate alternate routes, wherein the action decision includes an action decision to recommend one or more of the alternate routes and one or more alternate current driver for the motor vehicle based on predicted safety performance of the first driver user and the plurality of passengers as determined by the predicting safety performance.

12. The computer implemented method of claim 1, wherein sensors disposed to output data indicative of vehicle driver performance of a first vehicle include (a) an engine sensor disposed in the first vehicle, (b) a brake sensor disposed in the first vehicle, (c) a GPS sensor disposed in the first sensor, (d) a first camera sensor disposed in the first vehicle, (e) a second camera sensor disposed at a traffic intersection for capture of image data representing the first vehicle and (f) a touch pad sensor disposed in a mobile client computer device of the first driver user, wherein the method includes processing image data output by the first camera sensor to identify errors of the first driver user characterized by the first driver user not looking at a roadway ahead, wherein the method includes processing image data output by the second camera sensor to identify traffic infractions of the first driver user occurring at the traffic intersection, and the method includes processing data of the touch pad sensor to identify errors of the first driver user characterized by the first driver user texting while driving.

13. The computer implemented method of claim 1, wherein the first driving environment classification parameter is a time of day driving environment parameter and wherein the second driving environment classification parameter is a weather classification parameter.

14. The computer implemented method of claim 1, wherein method includes recording for respective event flags of the error event flags values of a values of a third driving environment classification parameter, values of a fourth driving environment classification parameter, values of a fifth driving environment classification parameter, and values of a sixth driving environment classification parameter, wherein the first driving environment classification parameter is a vehicle type classification parameter such that parameter values thereof specify a vehicle type of the first vehicle, wherein the second driving environment classification parameter is a last activity classification parameter such that parameter values thereof specify a most recent activity of the first driver user prior to the first trip, wherein the third driving environment classification parameter is a last activity classification parameter such that parameter values thereof specify whether a current roadway is a highway, or a city roadway, wherein the fourth driving environment classification parameter is a time of day driving environment classification parameter such that parameter values thereof specify whether a current time of day is day or night, wherein the fifth driving environment classification parameter is a weather driving environment classification parameter such that parameter values thereof specify whether a current weather being experienced by the first vehicle is rainy, snowing, or clear, and wherein the sixth driving environment classification parameter is a length driving environment classification parameter such that parameter values thereof are in dependence on a current elapsed time from commencement of the first trip.

15. The computer implemented method of claim 1, wherein the returning an action decision is in dependence on configuration data defined by a user, wherein the configuration data specifies one or more criterion that qualifies the first driver user for acting as a driver in the second trip.

16. The computer implemented method of claim 1, wherein the returning an action decision is in dependence on configuration data defined by an administrator user based on data entered into a user interface by the administrator user, wherein the configuration data specifies a plurality of criterion for qualifying the first driver user as a driver in the second trip, wherein the plurality of criterion include each of the following criterion:

(a) that a predicted performance of the first driver user when driving a first type of vehicle specified by the administrator user exceeds a performance threshold specified by the administrator user;

(b) that a predicted performance of the first driver user when driving on a roadway type specified by the administrator user exceeds a performance threshold specified by the administrator user;

(c) that a predicted performance of the first driver user when driving during a time of day specified by the administrator user exceeds a performance threshold specified by the administrator user;

(d) that a predicted performance of the first driver user when driving under weather conditions specified by the administrator user exceeds a performance threshold specified by the administrator user.

17. The computer implemented method of claim 1, wherein the method includes tagging the event flags of the error event flags with values of the first driving environment classification parameter and with values of the second driving environment classification parameter, wherein the first driving environment classification parameter is a time of day driving environment parameter and wherein the second driving environment classification parameter is a weather classification parameter, wherein performing the predicting includes using a predictive model that has been trained by machine learning processes including application of iteratively applied sets of training data, wherein the second trip is a proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes identifying a plurality of passengers located in the motor vehicle and wherein the method includes obtaining candidate alternate routes for the second trip, and predicting safety performance of the first driver user and the plurality of passengers for alternate routes of the candidate alternate routes, wherein the action decision is an action decision to recommend one or more of the alternate routes and one or more alternate current driver for the motor vehicle based on predicted safety performance of the first driver user and the plurality of passengers as determined by the predicting safety performance.

18. A computer program product comprising:

a computer readable storage medium readable by one or more processing circuit and storing instructions for execution by one or more processor for performing a method comprising;

obtaining sensor data from sensors disposed to output data indicative of vehicle driver performance of a first vehicle, the first vehicle having for a first trip an associated first driver user who drives the first vehicle during the first trip;

processing the sensor data to return error event flags that indicate errors of the first driver user when driving the first vehicle during the first trip;

recording for respective event flags of the error event flags values of a first driving environment classification parameter and values of a second driving environment classification parameter;

predicting a driving performance of the first driver user for a second trip, the second trip being a proposed trip to occur after the first trip, the predicting being in dependence on the error event flags, the values of a first driving environment classification parameter and the values of a second driving environment classification parameter, wherein the error event flags, the values of a first driving environment classification parameter and the values of a second driving environment classification parameter define historical driving performance data of the first driver user, wherein the second trip is proposed for performance by a motor vehicle being driven by the first driver user; and

returning an action decision in dependence on the predicting, wherein the action decision includes an action decision to limit performance of the motor vehicle so that performance of the motor vehicle is modified when driven by the first driver user.

19. The computer program product of claim 18, wherein the second trip is a proposed for performance by a motor vehicle being driven by the first driver user, wherein the method includes obtaining candidate alternate routes for the second trip, and predicting safety performance of the first driver user for alternate routes of the candidate alternate routes, wherein the action decision includes an action decision to recommend one or more of the alternate routes based on predicted safety performance of the first driver user as determined by the predicting safety performance.

20. A system comprising:

a memory;

at least one processor in communication with memory; and

program instructions executable by one or more processor via the memory to perform a method comprising;

obtaining sensor data from sensors disposed to output data indicative of vehicle driver performance of a first vehicle, the first vehicle having for a first trip an associated first driver user who drives the first vehicle during the first trip;

processing the sensor data to return error event flags that indicate errors of the first driver user when driving the first vehicle during the first trip;

recording for respective event flags of the error event flags values of a first driving environment classification parameter and values of a second driving environment classification parameter;

predicting a driving performance of the first driver user for a second trip, the second trip being a proposed trip to occur after the first trip, the predicting being in dependence on the error event flags, the values of a first driving environment classification parameter and the values of a second driving environment classification parameter, wherein the error event flags, the values of a first driving environment classification parameter and the values of a second driving environment classification parameter define historical driving performance data of the first driver user, wherein performing the predicting includes using a predictive model that has been trained by machine learning processes including application of iteratively applied sets of training data, wherein respective applied sets of training data for respective iteration of the iteratively applied set of training data include (a) an environment condition values dataset specifying environmental conditions for the first vehicle for a time segment of the first trip; and

(b) an error event flag dataset specifying error events of the first driver user during the time segment of the first trip; and

returning an action decision in dependence on the predicting.