# US 11,897,514 B2 · Ride share drop off selection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,897,514 B2 - Document ID: US11897514B2 - Status: Granted - Kind: B2 - Application number: 17/524,313 - Filed: 2021-11-11 - Published: 2023-05-11 - Granted: 2024-02-13 - Earliest claimed: 2021-11-11 - Assignees: GM Cruise Holdings LLC - Applicants: GM Cruise Holdings LLC - Inventors: Hariprasad Govardhanam - Web page: https://patentgrep.com/patent/US11897514B2 ## CPC - B60W 60/00253: {Taxi operations} - G06Q 10/0631: Resource planning, allocation, distributing or scheduling for enterprises or organisations - G06V 10/40: Extraction of image or video features - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - B60W 2554/4029: Pedestrians - B60W 2554/406: Traffic density - B60W 2555/20: Ambient conditions, e.g. wind or rain - 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 - 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 ## Abstract The disclosed technology provides solutions for improving passenger drop-off functions implemented by an autonomous vehicle (AV). In some implementations, a process of the disclosed technology can include steps for collecting environmental data about an environment around an autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle, processing the environmental data to generate an area grid comprising a plurality of grid sections, and associating, based on the environmental data, one or more features with each of the plurality of grid sections. Systems and machine-readable media are also provided. ## Claims ### Claim 1 An autonomous vehicle, comprising: one or more environmental sensors; at least one memory; and at least one processor coupled to the at least one memory and the one or more environmental sensors, the at least one processor configured to: collect environmental data, via the environmental sensors, about an environment around the autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle; project the environmental data onto an area grid comprising a plurality of grid sections, and wherein each grid section corresponds with a different area of the roadway; associate, based on the environmental data, each of the plurality of grid sections with one or more features or objects; and generate a drop-off feasibility score for at least one of the plurality of grid sections based on the one or more features associated with each of the plurality of grid sections. ### Claim 2 (depends on 1) The autonomous vehicle of claim 1, wherein the at least one processor is further configured to: determine an optimal drop-off location on the roadway for a passenger of the autonomous vehicle, based on the drop-off feasibility score for each of the plurality of grid sections. ### Claim 3 (depends on 2) The autonomous vehicle of claim 2, wherein determining the optimal drop-off location for the passenger is further based on a location requested by the passenger. ### Claim 4 (depends on 2) The autonomous vehicle of claim 2, wherein determining the optimal drop-off location is further based on preference information associated with the passenger. ### Claim 5 (depends on 1) The autonomous vehicle of claim 1, wherein the one or more features comprises: a lighting condition, an indication of adjacent structures, an indication of roadway conditions, or a combination thereof. ### Claim 6 (depends on 1) The autonomous vehicle of claim 1, wherein the one or more features comprises a number of objects associated with a grid section, a number of pedestrians associated with the grid section, or a combination thereof. ### Claim 7 A computer-implemented method, comprising: collecting environmental data, using one or more environmental sensors, about an environment around an autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle; processing the environmental data onto an area grid comprising a plurality of grid sections, and wherein each grid section corresponds with a different area of the roadway; associating, based on the environmental data, each of the plurality of grid sections with one or more features or objects; and generating a drop-off feasibility score for at least one of the plurality of grid sections based on the one or more features associated with each of the plurality of grid sections. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, further comprising: determining an optimal drop-off location on the roadway for a passenger of the autonomous vehicle, based on the drop-off feasibility score for each of the plurality of grid sections. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein determining the optimal drop-off location for the passenger is further based on a location requested by the passenger. ### Claim 10 (depends on 8) The computer-implemented method of claim 8, wherein determining the optimal drop-off location is further based on preference information associated with the passenger. ### Claim 11 (depends on 7) The computer-implemented method of claim 7, wherein the one or more features comprises: a lighting condition, an indication of adjacent structures, an indication of roadway conditions, or a combination thereof. ### Claim 12 (depends on 7) The computer-implemented method of claim 7, wherein the one or more features comprises a number of objects associated with a grid section, a number of pedestrians associated with the grid section, or a combination thereof. ### Claim 13 A non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to: collect environmental data, using one or more environmental sensors, about an environment around an autonomous vehicle, wherein the environmental data comprises data pertaining to a roadway navigated by the autonomous vehicle; project the environmental data onto an area grid comprising a plurality of grid sections, and wherein each grid section corresponds with a different area of the roadway; associate, based on the environmental data, each of the plurality of grid sections with one or more features or objects; and generate a drop-off feasibility score for at least one of the plurality of grid sections based on the one or more features associated with each of the plurality of grid sections. ### Claim 14 (depends on 13) The non-transitory computer-readable storage medium of claim 13, wherein the at least one instruction is further configured to cause the computer or processor to: determine an optimal drop-off location on the roadway for a passenger of the autonomous vehicle, based on the drop-off feasibility score for each of the plurality of grid sections. ### Claim 15 (depends on 14) The non-transitory computer-readable storage medium of claim 14, wherein determining the optimal drop-off location for the passenger is further based on a location requested by the passenger. ### Claim 16 (depends on 14) The non-transitory computer-readable storage medium of claim 14, wherein determining the optimal drop-off location is further based on preference information associated with the passenger. ### Claim 17 (depends on 13) The non-transitory computer-readable storage medium of claim 13, wherein the one or more features comprises: a lighting condition, an indication of adjacent structures, an indication of roadway conditions, or a combination thereof.