# US 10,479,356 B1 · Road segment similarity determination > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,479,356 B1 - Document ID: US10479356B1 - Status: Granted - Kind: B1 - Application number: 16/104,842 - Filed: 2018-08-17 - Granted: 2019-11-19 - Earliest claimed: 2018-08-17 - Assignees: Lyft, Inc. - Applicants: Lyft, Inc. - Inventors: Asif Haque; Mark Douglas Huyler; Gerard Joyce; Ying Liu; David Tse-Zhou Lu; Sameer Qureshi; Vinay Shet - Web page: https://patentgrep.com/patent/US10479356B1 ## CPC - B60W 30/095: Predicting travel path or likelihood of collision - G01C 21/28: with correlation of data from several navigational instruments - B60W 30/09: Taking automatic action to avoid collision, e.g. braking and steering - B60W 40/06: Road conditions - B60W 10/04: including control of propulsion units - B60W 10/18: including control of braking systems - B60W 10/20: including control of steering systems - B60W 2420/403: Image sensing, e.g. optical camera - B60W 2420/408: Radar; Laser, e.g. lidar - G01S 17/93: for anti-collision purposes - G05D 1/0238: {using obstacle or wall sensors (G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93)} - G05D 1/0246: {using a video camera in combination with image processing means} - G05D 1/0278: {using satellite positioning signals, e.g. GPS} - 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 - 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) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES ## Abstract Systems, methods, and non-transitory computer-readable media can determine a road segment. A set of features associated with the road segment can be determined based at least in part on data captured by one or more sensors of a vehicle. A level of similarity between the road segment and each of a set of road segment types can be determined by comparing the set of features to features associated with each of the set of road segment types. The road segment can be classified as a road segment type based on the level of similarity. Scenario information associated with the road segment can be determined based on the classified road segment type. ## Claims ### Claim 1 A computer-implemented method comprising: determining, by a computing system, a road segment; determining, by the computing system, a set of features associated with the road segment based at least in part on data captured by one or more sensors of a vehicle; determining, by the computing system, a level of similarity between the road segment and each of a set of road segment types by comparing the set of features to features associated with each of the set of road segment types; classifying, by the computing system, the road segment as a road segment type based on the level of similarity; determining, by the computing system, scenario information associated with the road segment based on the road segment type; logging, by the computing system, the road segment type and the scenario information as being associated with the road segment in a scenario information database; and generating, by the computing system, an instruction associated with operation of the vehicle based at least in part on the road segment type of the road segment and the scenario information. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the determining scenario information comprises: receiving, by the computing system, the set of features corresponding to the road segment based at least in part on the data captured by one or more sensors; determining, by the computing system, at least one scenario that is associated with the set of features; logging, by the computing system, the at least one scenario associated with the road segment and the road segment type in the scenario information database; and determining, by the computing system, the scenario information associated with the road segment type in the scenario information database. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the set of features corresponding to the road segment identify one or more of: at least one object, at least one road feature, and at least one contextual feature. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein determining the level of similarity between the road segment and each of the set of road segment types comprises: determining, by the computing system, that at least a threshold number of features in the set of features associated with the road segment and features in a set of features associated with a road segment type match. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising determining scenario information associated with the road segment based on the road segment type, wherein the determining scenario information comprises: determining, by the computing system, a risk profile associated with the road segment type, wherein the risk profile is based on scenarios and corresponding scenario exposure rates for the road segment type. ### Claim 6 (depends on 5) The computer-implemented method of claim 5, further comprising: associating, by the computing system, the risk profile associated with the road segment type with the road segment. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, further comprising: storing, by the computing system, information describing the association between the road segment and the risk profile in the scenario information database. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising: associating, by the computing system, instructions for modifying an operation of an autonomous vehicle with the road segment. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, further comprising: generating, by the computing system, a value of aggregate risk for a fleet based in part on classifying the road segment as the road segment type. ### Claim 10 (depends on 1) The computer-implemented method of claim 1, further comprising: determining, by the computing system, an aggregate scenario exposure for a first geographic location; determining, by the computing system, a threshold level of similarity between the first geographic location and a second geographic location based on a comparison of respective road segments of the first geographic location and the second geographic location; and associating, by the computing system, the aggregate scenario exposure with the second geographic location. ### Claim 11 A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: determining a road segment; determining a set of features associated with the road segment based at least in part on data captured by one or more sensors of a vehicle; determining a level of similarity between the road segment and each of a set of road segment types by comparing the set of features to features associated with each of the set of road segment types; classifying the road segment as a road segment type based on the level of similarity; determining scenario information associated with the road segment based on the road segment type; logging the road segment type and the scenario information as being associated with the road segment in a scenario information database; and generating an instruction associated with operation of the vehicle based at least in part on the road segment type of the road segment and the scenario information. ### Claim 12 (depends on 11) The system of claim 11, wherein the determining scenario information causes the system to perform: receiving the set of features corresponding to the road segment based at least in part on the data captured by one or more sensors; determining at least one scenario that is associated with the set of features; logging the at least one scenario associated with the road segment and the road segment type in the scenario information database; and determining the scenario information associated with the road segment type in the scenario information database. ### Claim 13 (depends on 12) The system of claim 12, wherein the set of features corresponding to the road segment identify one or more of: at least one object, at least one road feature, and at least one contextual feature. ### Claim 14 (depends on 11) The system of claim 11, wherein determining the level of similarity between the road segment and each of the set of road segment types causes the system to perform: determining that at least a threshold number of features in the set of features associated with the road segment and features in a set of features associated with a road segment type match. ### Claim 15 (depends on 11) The system of claim 11, further comprising determining scenario information associated with the road segment based on the road segment type, wherein the determining scenario information causes the system to perform: determining a risk profile associated with the road segment type, wherein the risk profile is based on scenarios and corresponding exposure rates for the road segment type. ### Claim 16 A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: determining a road segment; determining a set of features associated with the road segment based at least in part on data captured by one or more sensors of a vehicle; determining a level of similarity between the road segment and each of a set of road segment types by comparing the set of features to features associated with each of the set of road segment types; classifying the road segment as a road segment type based on the level of similarity; classifying the road segment as a road segment type based on the level of similarity; determining scenario information associated with the road segment based on the road segment type; logging the road segment type and the scenario information as being associated with the road segment in a scenario information database; and generating an instruction associated with operation of the vehicle based at least in part on the road segment type of the road segment and the scenario information. ### Claim 17 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein the determining scenario information causes the computing system to perform: receiving the set of features corresponding to the road segment based at least in part on the data captured by one or more sensors; determining at least one scenario that is associated with the set of features; logging the at least one scenario associated with the road segment and the road segment type in the scenario information database; and determining the scenario information associated with the road segment type in the scenario information database. ### Claim 18 (depends on 17) The non-transitory computer-readable storage medium of claim 17, wherein the set of features corresponding to the road segment identify one or more of: at least one object, at least one road feature, and at least one contextual feature. ### Claim 19 (depends on 16) The non-transitory computer-readable storage medium of claim 16, wherein determining the level of similarity between the road segment and each of the set of road segment types causes the computing system to perform: determining that at least a threshold number of features in the set of features associated with the road segment and features in a set of features associated with a road segment type match. ### Claim 20 (depends on 16) The non-transitory computer-readable storage medium of claim 16, further comprising determining scenario information associated with the road segment based on the road segment type, wherein the determining scenario information causes the computing system to perform: determining a risk profile associated with the road segment type, wherein the risk profile is based on scenarios and corresponding exposure rates for the road segment type.