# US 2025/0085128 A1 · Systems and methods for inferring information about stationary elements based on semantic relationships > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0085128 A1 - Document ID: US20250085128A1 - Status: Published application - Kind: A1 - Application number: 18/959,521 - Filed: 2024-11-25 - Published: 2025-03-13 - Earliest claimed: 2020-06-30 - Assignees: Lyft, Inc. - Applicants: Lyft, Inc. - Inventors: Ritwik Subir Das; Kris Richard Efland; Nadha Nafeeza Gafoor; Nastaran Ghadar; Meng Tao - Web page: https://patentgrep.com/patent/US20250085128A1 ## CPC - G01C 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera} - G01C 21/3492: {employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)} - G01C 21/3807: {characterised by the type of data} - G01C 21/3833: {characterised by the source of data} - G01C 21/3896: {Transmission of map data from central databases} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 20/582: {of traffic signs} - G06V 20/584: {of vehicle lights or traffic lights} - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - 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) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Examples disclosed herein may involve a computing system configured to (i) after one or more sensor-equipped vehicles have traversed a real-world environment and captured sensor data that is representative of the real-world environment, perform an analysis of the sensor data; (ii) based on the analysis of the sensor data, derive a set of information about a traffic light within the real-world environment that includes one or more of (a) signal-face information that comprises an identification of each signal face of the traffic light or (b) traffic-rule information that comprises an indication of at least one traffic rule that is applicable to the traffic light; and (iii) encode the derived set of information about the traffic light into a map for the real-world environment. ## Claims ### Claim 1 A computer-implemented method comprising: after one or more sensor-equipped vehicles have traversed a real-world environment and captured sensor data that is representative of the real-world environment, performing an analysis of the sensor data; based on the analysis of the sensor data, deriving a set of information about a traffic light within the real-world environment that includes one or more of (i) signal-face information that comprises an identification of each signal face of the traffic light or (ii) traffic-rule information that comprises an indication of at least one traffic rule that is applicable to the traffic light; and encoding the derived set of information about the traffic light into a map for the real-world environment. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the signal-face information further comprises an indication of at least one activation sequence for the signal faces of the traffic light. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the at least one activation sequence for the signal faces of the traffic light comprises multiple activation sequences corresponding to different times of day. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the signal-face information further comprises, for each signal face of the traffic light, an indication of a respective length of time during which the signal face is activated. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the at least one traffic rule that is applicable to the traffic light comprises a traffic rule that is applicable to a given signal face of the traffic light. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the derived set of information about the traffic light further includes lane-control information that comprises an indication of a given lane that is controlled by a given signal face of the traffic light. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the identification of each signal face of the traffic light comprises (i) a location of the signal face within the traffic light and (ii) a type of the signal face. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the sensor data is captured over a multi-day period of time. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein deriving the set of information about the traffic light comprises: deriving the signal-face information by applying computer-vision models to the sensor data that is captured over the multi-day period of time. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, wherein deriving the set of information about the traffic light comprises: deriving the traffic-rule information by (i) detecting a semantic relationship between the traffic light and one or more other stationary elements within the real-world environment, (ii) performing an analysis of the one or more other stationary elements, (iii) based on the analysis of the one or more other stationary elements, deriving the indication of the at least one traffic rule that is applicable to the traffic light. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the analysis of the one or more other stationary elements comprises an analysis of one or both of text or images appearing on a traffic sign. ### Claim 12 A non-transitory computer-readable medium comprising program instructions stored thereon that are executable to cause a computing system to: after one or more sensor-equipped vehicles have traversed a real-world environment and captured sensor data that is representative of the real-world environment, perform an analysis of the sensor data; based on the analysis of the sensor data, derive a set of information about a traffic light within the real-world environment that includes one or more of (i) signal-face information that comprises an identification of each signal face of the traffic light or (ii) traffic-rule information that comprises an indication of at least one traffic rule that is applicable to the traffic light; and encode the derived set of information about the traffic light into a map for the real-world environment. ### Claim 13 (depends on 12) The computer-readable medium of claim 12, wherein the signal-face information further comprises an indication of at least one activation sequence for the signal faces of the traffic light. ### Claim 14 (depends on 13) The computer-readable medium of claim 13, wherein the at least one activation sequence for the signal faces of the traffic light comprises multiple activation sequences corresponding to different times of day. ### Claim 15 (depends on 12) The computer-readable medium of claim 12, wherein the signal-face information further comprises, for each signal face of the traffic light, an indication of a respective length of time during which the signal face is activated. ### Claim 16 (depends on 12) The computer-readable medium of claim 12, wherein the at least one traffic rule that is applicable to the traffic light comprises a traffic rule that is applicable to a given signal face of the traffic light. ### Claim 17 (depends on 12) The computer-readable medium of claim 12, wherein the derived set of information about the traffic light further includes lane-control information that comprises an indication of a given lane that is controlled by a given signal face of the traffic light. ### Claim 18 (depends on 12) The computer-readable medium of claim 12, wherein the identification of each signal face of the traffic light comprises (i) a location of the signal face within the traffic light and (ii) a type of the signal face. ### Claim 19 (depends on 12) The computer-readable medium of claim 12, wherein the sensor data is captured over a multi-day period of time. ### Claim 20 A computing system comprising: at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor such that the computing system is capable of: after one or more sensor-equipped vehicles have traversed a real-world environment and captured sensor data that is representative of the real-world environment, performing an analysis of the sensor data; based on the analysis of the sensor data, deriving a set of information about a traffic light within the real-world environment that includes one or more of (i) signal-face information that comprises an identification of each signal face of the traffic light or (ii) traffic-rule information that comprises an indication of at least one traffic rule that is applicable to the traffic light; and encoding the derived set of information about the traffic light into a map for the real-world environment.