Regional driving trend modification using autonomous vehicles

• US 9,349,284 B2
• Filed: 04/24/2014
• Issued: 05/24/2016
• Est. Priority Date: 04/24/2014
• Status: Active Grant

• Current Assignee: Slingshot IOT LLC
• Original Assignee: International Business Machines Corporation
• Inventors: Cudak, Gary D., Hardee, Christopher J., Roberts, Adam, Rodriguez, Adrian X.
• Primary Examiner(s): CHEUNG, Calvin
• Assistant Examiner(s): SCHNEIDER, PAULA LYNN
• Application Number:
  US14/260,397
• Publication Number:
  US 20150308837A1
• Time in Patent Office: 761 Days
• Field of Search: 701/23
• US Class Current: 1/1
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D

Abstract

Embodiments of the present invention disclose a method, computer program product, and a computer system for modifying regional driving habits. A computer determines a first value associated with a regional driving habit for a region. The computer determines a second value associated with a target driving habit, wherein the second value is not substantially equivalent to the first value. The computer determines that a number of at least semi-autonomous vehicles in an area is above a threshold value. The computer transmits information instructing the number of at least semi-autonomous vehicles to operate based on the second value.

Claims

1. A computer program product for modifying regional driving habits, the computer program product comprising:

one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising;

program instructions to determine a first value associated with a regional driving habit for a region;

program instructions to determine a second value associated with a target driving habit, wherein the second value is not equivalent to the first value;

program instructions to determine that a number of at least semi-autonomous vehicles in an area is above a threshold value; and

program instructions to transmit information instructing the number of at least semi-autonomous vehicles to operate based on the second value.

2. The computer program product of claim 1, wherein the program instructions further comprise:

program instructions to receive data detailing the operation of the one or more at least partially manually operated vehicles related to the regional driving habit with which the first value is associated; and

program instructions to update the first value based, at least in part, on the data detailing the operation of the one or more at least partially manually operated vehicles.

3. The computer program product of claim 2, wherein the program instructions further comprise:

program instructions to determine that a second number of at least semi-autonomous vehicles in the area is below the threshold value; and

program instructions to transmit, by one or more computer processors, information instructing the number of at least semi-autonomous vehicles to operate based on the updated first value.

4. The computer program product of claim 2, wherein the data detailing the operation of one or more at least partially manually operated vehicles is collected using one or more sensors mounted on one or more of the at least semi-autonomous vehicles.

5. The computer program product of claim 1, wherein the area is a subset of the region.

6. The computer program product of claim 1, wherein the first value and the second value are numerical representations of a quantifiable driving habit.

7. A computer system for modifying regional driving habits, the computer system comprising:

one or more computer processors;

one or more computer-readable storage media;

program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising;

program instructions to determine a first value associated with a regional driving habit for a region;

program instructions to determine a second value associated with a target driving habit, wherein the second value is not equivalent to the first value;

program instructions to determine that a number of at least semi-autonomous vehicles in an area is above a threshold value; and

program instructions to transmit information instructing the number of at least semi-autonomous vehicles to operate based on the second value.

8. The computer system of claim 7, wherein the program instructions further comprise:

program instructions to receive data detailing the operation of the one or more at least partially manually operated vehicles related to the regional driving habit with which the first value is associated; and

program instructions to update the first value based, at least in part, on the data detailing the operation of the one or more at least partially manually operated vehicles.

9. The computer system of claim 8, wherein the program instructions further comprise:

program instructions to determine that a second number of at least semi-autonomous vehicles in the area is below the threshold value; and

program instructions to transmit, by one or more computer processors, information instructing the number of at least semi-autonomous vehicles to operate based on the updated first value.

10. The computer system of claim 8, wherein the data detailing the operation of one or more at least partially manually operated vehicles is collected using one or more sensors mounted on one or more of the at least semi-autonomous vehicles.

11. The computer system of claim 7, wherein the area is a subset of the region.

12. The computer system of claim 7, wherein the first value and the second value are numerical representations of a quantifiable driving habit.