# US 9,751,532 B2 · Controlling spacing of self-driving vehicles based on social network relationships > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,751,532 B2 - Document ID: US9751532B2 - Status: Granted - Kind: B2 - Application number: 14/924,163 - Filed: 2015-10-27 - Published: 2017-04-27 - Granted: 2017-09-05 - Earliest claimed: 2015-10-27 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Michael S. Gordon; James R. Kozloski; Ashish Kundu; Peter K. Malkin; Clifford A. Pickover - Web page: https://patentgrep.com/patent/US9751532B2 ## CPC - G08G 1/22: {Platooning, i.e. convoy of communicating vehicles} - B60W 60/0051: {from occupants to vehicle} - B60W 60/0053: {from vehicle to occupant} - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - B60W 2556/10: Historical data - G01C 21/36: Input/output arrangements for on-board computers - G06F 21/62: Protecting access to data via a platform, e.g. using keys or access control rules - G06Q 10/08: Logistics, e.g. warehousing, loading or distribution; Inventory or stock management - G06Q 10/1093: Calendar-based scheduling for persons or groups - G06Q 50/00: Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism (healthcare informatics G16H) - G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083) - H04L 63/10: {for controlling access to devices or network resources} - H04L 63/102: {Entity profiles} - H04L 63/20: {for managing network security; network security policies in general (filtering policies H04L63/0227)} - H04M 1/72403: with means for local support of applications that increase the functionality - H04M 1/725: Cordless telephones (user interfaces specially adapted therefor H04M1/724) - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) ## Abstract A method and/or computer program product controls a physical spacing between self-driving vehicles (SDVs). One or more processors receive a social network node graph. The social network node graph describes a graphical distance between a first node on the social network node graph and a second node on the social network node graph. The first node represents a first passenger in a first SDV; the second node represents a second passenger in a second SDV; and the graphical distance between the first node and the second node describes a relationship level in a social network between the first passenger and the second passenger. An SDV on-board computer on at least one of the first SDV and the second SDV adjusts a physical spacing between the first SDV and the second SDV proportional to the graphical distance between the first node and the second node. ## Claims ### Claim 1 A method for controlling a physical spacing between self-driving vehicles (SDVs), the method comprising: receiving, by one or more processors, a social network node graph, wherein the social network node graph describes a graphical distance between a first node on the social network node graph and a second node on the social network node graph, wherein the first node represents a first passenger in a first SDV, wherein the second node represents a second passenger in a second SDV, and wherein the graphical distance between the first node and the second node describes a relationship level in a social network between the first passenger and the second passenger; and adjusting, by an SDV on-board computer on at least one of the first SDV and the second SDV, a physical spacing between the first SDV and the second SDV proportional to the graphical distance between the first node and the second node. ### Claim 2 (depends on 1) The method of claim 1, further comprising: altering, by the SDV on-board computer on said at least one of the first SDV and the second SDV, a route of travel based on the relationship level between the first passenger and the second passenger. ### Claim 3 (depends on 1) The method of claim 1, wherein the social network node graph further describes a graphical distance between a third node on the social network node graph and the second node on the social network node graph, wherein the third node represents a third passenger in the first SDV, wherein the graphical distance between the third node and the second node describes a relationship level in the social network between the third passenger and the second passenger, and wherein the method further comprises: weighting, by one or more processors, the first node and the third node based on the relationship level in the social network between the first passenger and the second passenger versus the relationship level in the social network between the third passenger and the second passenger; and adjusting, by an SDV on-board computer on at least one of the first SDV and the second SDV, a physical spacing between the first SDV and the second SDV based on the weighted first and third nodes. ### Claim 4 (depends on 1) The method of claim 1, further comprising: detecting, based on sensor readings from a roadway sensor, a current roadway condition of a roadway upon which the first SDV and the second SDV are traveling; and further adjusting, by the SDV on-board computer on the first SDV, the physical spacing between the first SDV and the second SDV based on the current roadway condition of the roadway upon which the first SDV and the second SDV are traveling. ### Claim 5 (depends on 1) The method of claim 1, wherein the first SDV is traveling on a roadway in manual mode and the second SDV is traveling on the roadway in autonomous mode, and wherein the method further comprises: retrieving, by one or more processors, driver profile information about a human driver of the first SDV; assigning, by one or more processors, the human driver of the first SDV to a cohort of drivers who have traveled on the roadway in other SDVs, wherein the human driver of the first SDV shares more than a predetermined quantity of traits with members of the cohort of drivers who have traveled on the roadway in the other SDVs; retrieving, by one or more processors, traffic pattern data for the other SDVs as they traveled on the roadway; examining, by one or more processors, the traffic pattern data to determine a first traffic flow of the other SDVs while operating in autonomous mode on the roadway; examining, by one or more processors, the traffic pattern data to determine a second traffic flow of the other SDVs while operating in 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, changing, by the operational mode device, the operational mode of the first SDV from manual mode to autonomous mode. ### Claim 6 (depends on 1) The method of claim 1, wherein the first SDV and the second SDV are traveling on the roadway in autonomous mode, and wherein the method further comprises: 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 the predefined level, further adjusting, by an SDV on-board computer on at least one of the first SDV and the second SDV, the physical spacing between the first SDV and the second SDV. ### Claim 7 (depends on 1) The method of claim 1, wherein the first SDV and the second SDV are traveling on the roadway in autonomous mode, and wherein the method further comprises: receiving, by one or more processors, operational readings from one or more SDV operational sensors on the first SDV, wherein the SDV operational sensors detect a current state of mechanical equipment on the first SDV; detecting, by the one or more processors and based on received operational readings, a mechanical fault with the mechanical equipment on the first SDV; and in response to detecting the mechanical fault with the mechanical equipment on the first SDV, further adjusting, by the SDV on-board computer on said at least one of the first SDV and the second SDV, the physical spacing between the first SDV and the second SDV. ### Claim 8 A computer program product for controlling a physical spacing between self-driving vehicles (SDVs), 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 a social network node graph, wherein the social network node graph describes a graphical distance between a first node on the social network node graph and a second node on the social network node graph, wherein the first node represents a first passenger in a first SDV, wherein the second node represents a second passenger in a second SDV, and wherein the graphical distance between the first node and the second node describes a relationship level in a social network between the first passenger and the second passenger; and adjusting, via an SDV on-board computer on at least one of the first SDV and the second SDV, a physical spacing between the first SDV and the second SDV proportional to the graphical distance between the first node and the second node. ### Claim 9 (depends on 8) The computer program product of claim 8, wherein the method further comprises: altering, via the SDV on-board computer on said at least one of the first SDV and the second SDV, a route of travel based on the relationship level between the first passenger and the second passenger. ### Claim 10 (depends on 8) The computer program product of claim 8, wherein the social network node graph further describes a graphical distance between a third node on the social network node graph and the second node on the social network node graph, wherein the third node represents a third passenger in the first SDV, wherein the graphical distance between the third node and the second node describes a relationship level in the social network between the third passenger and the second passenger, and wherein the method further comprises: weighting the first node and the third node based on the relationship level in the social network between the first passenger and the second passenger versus the relationship level in the social network between the third passenger and the second passenger; and adjusting, via an SDV on-board computer on at least one of the first SDV and the second SDV, a physical spacing between the first SDV and the second SDV based on the weighted first and third nodes. ### Claim 11 (depends on 8) The computer program product of claim 8, wherein the method further comprises: detecting, based on sensor readings from a roadway sensor, a current roadway condition of a roadway upon which the first SDV and the second SDV are traveling; and further adjusting the physical spacing between the first SDV and the second SDV based on the current roadway condition of the roadway upon which the first SDV and the second SDV are traveling. ### Claim 12 (depends on 8) The computer program product of claim 8, wherein the first SDV is traveling on a roadway in manual mode and the second SDV is traveling on the roadway in autonomous mode, and wherein the method further comprises: retrieving driver profile information about a human driver of the first SDV; assigning the human driver of the first SDV to a cohort of drivers who have traveled on the roadway in other SDVs, wherein the human driver of the first SDV shares more than a predetermined quantity of traits with members of the cohort of drivers who have traveled on the roadway in the other SDVs; retrieving traffic pattern data for the other SDVs as they traveled on the roadway; examining the traffic pattern data to determine a first traffic flow of the other SDVs while operating in autonomous mode on the roadway; examining the traffic pattern data to determine a second traffic flow of the other SDVs while operating in 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, changing, via an operational mode device on the first SDV, an operational mode of the first SDV from manual mode to autonomous mode. ### Claim 13 (depends on 8) The computer program product of claim 8, wherein the first SDV and the second SDV are traveling on the roadway in autonomous mode, and 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 exceed the predefined level, further adjusting, via an SDV on-board computer on at least one of the first SDV and the second SDV, the physical spacing between the first SDV and the second SDV. ### Claim 14 (depends on 8) The computer program product of claim 8, wherein the first SDV and the second SDV are traveling on the roadway in autonomous mode, and wherein the method further comprises: receiving operational readings from one or more SDV operational sensors on the first SDV, wherein the SDV operational sensors detect a current state of mechanical equipment on the first SDV; detecting, based on received operational readings, a mechanical fault with the mechanical equipment on the first SDV; and in response to detecting the mechanical fault with the mechanical equipment on the first SDV, further adjusting, via the SDV on-board computer on said at least one of the first SDV and the second SDV, the physical spacing between the first SDV and the second SDV. ### Claim 15 A self-driving vehicle comprising: a processor, a computer readable memory, and a non-transitory computer readable storage medium; first program instructions to receive a social network node graph, wherein the social network node graph describes a graphical distance between a first node on the social network node graph and a second node on the social network node graph, wherein the self-driving vehicle is a first self-driving vehicle (SDV), wherein the first node represents a first passenger in a first SDV, wherein the second node represents a second passenger in a second SDV, and wherein the graphical distance between the first node and the second node describes a relationship level in a social network between the first passenger and the second passenger; and second program instructions to direct an SDV on-board computer on the first SDV to adjust a physical spacing between the first SDV and the second SDV proportional to the graphical distance between the first node and the second node; and wherein the first and second program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory. ### Claim 16 (depends on 15) The self-driving vehicle of claim 15, further comprising: third program instructions to alter a route of travel based on the relationship level between the first passenger and the second passenger; and wherein the third program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory. ### Claim 17 (depends on 15) The self-driving vehicle of claim 15, wherein the social network node graph further describes a graphical distance between a third node on the social network node graph and the second node on the social network node graph, wherein the third node represents a third passenger in the first SDV, wherein the graphical distance between the third node and the second node describes a relationship level in the social network between the third passenger and the second passenger, and wherein the self-driving vehicle further comprises: third program instructions to weight the first node and the third node based on the relationship level in the social network between the first passenger and the second passenger versus the relationship level in the social network between the third passenger and the second passenger; and fourth program instructions to adjust a physical spacing between the first SDV and the second SDV based on the weighted first and third nodes; and wherein the third and fourth program instructions are stored on the non-transitory computer readable storage medium for execution by one or more processors via the computer readable memory. ### Claim 18 (depends on 15) The self-driving vehicle of claim 15, further comprising: a roadway sensor for detecting a current roadway condition of a roadway upon which the self-driving vehicle is traveling; and an operational mode device for adjusting an operational mode of the self-driving vehicle based on the current roadway condition detected by the roadway sensor. ### Claim 19 (depends on 15) The self-driving vehicle of claim 15, wherein the self-driving vehicle is a first SDV that is traveling on a roadway in manual mode and while a second SDV is traveling on the roadway in autonomous mode, and wherein the self-driving vehicle further comprises: third program instructions to retrieve driver profile information about a human driver of the first SDV; fourth program instructions to assign the human driver of the first SDV to a cohort of drivers who have traveled on the roadway in other SDVs, wherein the human driver of the first SDV shares more than a predetermined quantity of traits with members of the cohort of drivers who have traveled on the roadway in the other SDVs; fifth program instructions to retrieve traffic pattern data for the other SDVs as they traveled on the roadway; sixth program instructions to examine the traffic pattern data to determine a first traffic flow of the other SDVs while operating in autonomous mode on the roadway; seventh program instructions to examine the traffic pattern data to determine a second traffic flow of the other SDVs while operating in manual mode on the roadway; and eighth program instructions to, in response to determining that the first traffic flow has a lower accident rate than the second traffic flow, change, via an operational mode device on the first SDV, the operational mode of the first SDV from manual mode to autonomous mode; and wherein the third, fourth, fifth, 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. ### Claim 20 (depends on 15) The self-driving vehicle of claim 15, wherein the self driving vehicle is traveling on a roadway while in autonomous mode, and wherein the self-driving vehicle further comprises: one or more SDV operational sensors for generating operational readings based on a current state of mechanical equipment on the self-driving vehicle, wherein the one or more SDV operational sensors detect a mechanical fault with the mechanical equipment on the self-driving vehicle, and wherein the SDV on-board computer on the first SDV, in response to detecting the mechanical fault with the mechanical equipment on the first SDV, further adjusts the physical spacing between the first SDV and the second SDV.