# US 10,872,143 B2 · Recognizing assigned passengers for autonomous vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,872,143 B2 - Document ID: US10872143B2 - Status: Granted - Kind: B2 - Application number: 16/725,256 - Filed: 2019-12-23 - Published: 2020-07-02 - Granted: 2020-12-22 - Earliest claimed: 2017-08-17 - Assignees: Waymo LLC - Applicants: Waymo LLC - Inventors: John Wesley Dyer; Luis Torres; Michael Epstein; Yu-Hsin Chen - Web page: https://patentgrep.com/patent/US10872143B2 ## CPC - G06F 21/44: Program or device authentication - B60R 21/01538: {for image processing, e.g. cameras or sensor arrays} - G06F 21/35: communicating wirelessly - G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G06V 40/10: Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands - G06V 40/103: {Static body considered as a whole, e.g. static pedestrian or occupant recognition} - G08B 13/19647: {Systems specially adapted for intrusion detection in or around a vehicle} - G08G 1/202: {Dispatching vehicles on the basis of a location, e.g. taxi dispatching} - H04L 9/3228: {One-time or temporary data, i.e. information which is sent for every authentication or authorization, e.g. one-time-password, one-time-token or one-time-key} - H04L 9/3234: {involving additional secure or trusted devices, e.g. TPM, smartcard, USB or software token (network architectures or network communication protocols for supporting authentication of entities using an additional device in a packet data network H04L63/0853)} - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - G06F 2221/2111: Location-sensitive, e.g. geographical location, GPS - G06V 40/161: {Detection; Localisation; Normalisation} - G06V 40/18: Eye characteristics, e.g. of the iris - H04L 2209/84: Vehicles - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - B60R: VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR - 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 - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - 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}) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract Aspects of the disclosure provide systems and methods for recognizing an assigned passenger. For instance, dispatching instructions to pick up a passenger at a pickup location are received. The instructions include authentication information for authenticating a client computing device associated with the passenger. A vehicle is maneuvered in an autonomous driving mode towards the pickup location. The client device is then authenticated. After authentication, a set of pedestrians within a predetermined distance of the vehicle are identified from sensor information generated by a sensor of the vehicle and location information is received over a period of time from the client device. The received location information is used to estimate a velocity of the passenger. This estimated velocity is used to identify a subset of set of pedestrians that is likely to be the passenger. The vehicle is stopped to allow the passenger to enter the vehicle based on the subset. ## Claims ### Claim 1 A method of recognizing an assigned passenger, the method comprising: receiving, by one or more processors of a vehicle, dispatching instructions to pick up the assigned passenger at a pickup location; maneuvering, by the one or more processors, the vehicle towards the pickup location in an autonomous driving mode; identifying, by the one or more processors, from sensor information generated by a sensor of the vehicle a set of pedestrians within a predetermined distance of the vehicle; receiving, by the one or more processors, location information identifying locations of a client computing device associated with the assigned passenger over a period of time; using, by the one or more processors, the received information to estimate a velocity of the client computing device; receiving, by the one or more processors, information indicative of the velocities of pedestrians of the set of pedestrians; comparing, by the one or more processors, the estimated velocity of the client computing device to the information indicative of the velocities of the pedestrians; and based on the comparing, identifying, by the one or more processors, one of the pedestrians of the set of pedestrians as the assigned passenger. ### Claim 2 (depends on 1) The method of claim 1, wherein the received information includes orientation information generated by a sensor of the client computing device, and the method further includes: determining an orientation of each pedestrian of the set of pedestrians; and comparing the orientation information with the determined orientations, and wherein the identifying the one of the pedestrians is further based on the comparing the orientation information with the determined orientations. ### Claim 3 (depends on 1) The method of claim 1, further comprising using the sensor information to detect a gaze direction for each pedestrian of the set of pedestrians, and wherein identifying the one of the pedestrians is further based on the gaze detection for each pedestrian. ### Claim 4 (depends on 1) The method of claim 1, further comprising using the sensor information to determine a number of other pedestrians corresponding to pedestrians within a predetermined distance of each pedestrian of the set of pedestrians, and wherein identifying the one of the pedestrians is further based on the determined number of other pedestrians within the predetermined distance of each pedestrian. ### Claim 5 (depends on 4) The method of claim 4, wherein the dispatching instructions further identify a number of passengers, and wherein identifying the one of the pedestrians is further based on the identified number of passengers. ### Claim 6 (depends on 1) The method of claim 1, wherein the set of pedestrians is updated as additional location information is received from the client computing device. ### Claim 7 (depends on 1) The method of claim 1, further comprising, based on the comparing, stopping, by the one or more processors, the vehicle to allow the assigned passenger to enter the vehicle. ### Claim 8 (depends on 7) The method of claim 7, wherein stopping the vehicle includes stopping the vehicle closer to a pedestrian of the set than to the pickup location. ### Claim 9 (depends on 7) The method of claim 7, wherein the dispatching instructions further include a pickup location, and stopping the vehicle includes stopping the vehicle before the vehicle reaches the pickup location. ### Claim 10 (depends on 1) The method of claim 1, further comprising: using the sensor information to identify a characteristic that is different between two or more pedestrians of the set of pedestrians; sending a request to the client device, the request including a question regarding the characteristic; and receiving a response from the client computing device, and wherein identifying the one of the pedestrians is further based on the response. ### Claim 11 (depends on 1) The method of claim 1, wherein the comparing includes inputting the estimated velocity and the information indicative of the velocities of the pedestrians into a model in order to identify a likelihood that each pedestrian of the set of pedestrians is the assigned passenger, and wherein identifying the one of the pedestrians is further based on the likelihood that each pedestrian of the set of pedestrians is the passenger. ### Claim 12 A system for recognizing an assigned passenger, the system comprising one or more hardware processors configured to: receive dispatching instructions to pick up the assigned passenger at a pickup location; maneuver the vehicle towards the pickup location in an autonomous driving mode; identify from sensor information generated by a sensor of the vehicle a set of pedestrians within a predetermined distance of the vehicle; receive location information identifying locations of a client computing device associated with the assigned passenger over a period of time; use the received information to estimate a velocity of the client computing device; receive information indicative of the velocities of pedestrians of the set of pedestrians; comparing, by the one or more processors, the estimated velocity of the client computing device to the information indicative of the velocities of the pedestrians; and based on the comparing, identify one of the pedestrians of the set of pedestrians as the assigned passenger. ### Claim 13 (depends on 12) The system of claim 12, wherein the received information includes orientation information generated by a sensor of the client computing device, and the one or more processors are further configured to: determine an orientation of each pedestrian of the set of pedestrians; and compare the orientation information with the determined orientations, and wherein the identifying the one of the pedestrians is further based on the comparing the orientation information with the determined orientations. ### Claim 14 (depends on 12) The system of claim 12, wherein the one or more processors are further configured to use the sensor information to detect a gaze direction for each pedestrian of the set of pedestrians, and wherein the identifying the one of the pedestrians is further based on the gaze detection for each pedestrian. ### Claim 15 (depends on 12) The system of claim 12, the one or more processors are further configured to use the sensor information to determine a number of other pedestrians corresponding to pedestrians within a predetermined distance of each pedestrian of the set of pedestrians, and wherein the identifying the one of the pedestrians is further based on the determined number of other pedestrians within the predetermined distance of each pedestrian. ### Claim 16 (depends on 15) The system of claim 15, wherein the dispatching instructions further identify a number of passengers, and wherein the identifying the one of the pedestrians is further based on the identified number of passengers. ### Claim 17 (depends on 12) The system of claim 12, wherein the set of pedestrians is updated as additional location information is received from the client computing device. ### Claim 18 (depends on 12) The system of claim 12, wherein the one or more processors are further configured to, based on the comparing, stop the vehicle to allow the assigned passenger to enter the vehicle. ### Claim 19 (depends on 12) The system of claim 12, further comprising: using the sensor information to identify a characteristic that is different between two or more pedestrians of the set of pedestrians; sending a request to the client device, the request including a question regarding the characteristic; and receiving a response from the client computing device, and wherein the identifying the one of the pedestrians is vehicle is further based on the response. ### Claim 20 (depends on 12) The system of claim 12, further comprising the vehicle.