# US 2020/0401135 A1 · Systems and Methods for Vehicle Motion Control With Interactive Object Annotation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401135 A1 - Document ID: US20200401135A1 - Status: Published application - Kind: A1 - Application number: 16/686,401 - Filed: 2019-11-18 - Published: 2020-12-24 - Earliest claimed: 2019-06-19 - Assignees: Uatc, LLC - Applicants: Uatc, LLC - Inventors: Sean Shanshi Chen; Samann Ghorbanian-Matloob - Web page: https://patentgrep.com/patent/US20200401135A1 ## CPC - G05D 1/0038: {by providing the operator with simple or augmented images from one or more cameras located onboard the vehicle, e.g. tele-operation (images analyzed by a computer and used for automatic navigation G05D1/0246)} - G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)} - B60W 60/0011: {involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles} - G05D 1/0212: {with means for defining a desired trajectory (involving a plurality of land vehicles G05D1/0287)} - G06N 20/00: Machine learning - B60W 2554/20: Static objects - B60W 2554/404: Characteristics - B60W 2556/45: External transmission of data to or from the vehicle - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems and methods for vehicle motion control with interactive object annotation are provided. A method can include obtaining data indicative of a plurality of objects within a surrounding environment of the autonomous vehicle. For example, the plurality of objects can include at least at one problem object encountered by the autonomous vehicle while navigating a planned route. The method can include determining a group of objects of the plurality of objects. For example, the group of objects can include the problem object and one or more other objects in proximity to the problem object. The method can include determining a classification update to be applied to the group of objects. The method can include applying the classification update to the group of objects. The method can include providing data indicative of the classification update for the group of objects to the autonomous vehicle for use in motion planning. ## Claims ### Claim 1 A computer-implemented method, the method comprising: obtaining, by a computing system comprising one or more computing devices that are remote from an autonomous vehicle, data indicative of a plurality of objects within a surrounding environment of the autonomous vehicle, the plurality of objects including at least at one problem object encountered by the autonomous vehicle while navigating a planned route; determining, by the computing system, a group of objects of the plurality of objects, the group of objects including the problem object and one or more other objects in proximity to the problem object; determining, by the computing system, a classification update to be applied to the group of objects; applying, by the computing system, the classification update to the group of objects; and providing, by the computing system, data indicative of the classification update for the group of objects to the autonomous vehicle for use in motion planning. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the problem object has been detected by the autonomous vehicle with incorrect object classification data. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the classification update to be applied to the group of objects comprises: an indication that the group of objects are static and the autonomous vehicle should pass on the left of the group of objects; an indication that the group of objects are static and the autonomous vehicle should pass on the right of the group of objects; or an indication that the group of objects are not static objects. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the classification update facilitates the autonomous vehicle generating motion planning to navigate past the group of objects and continue along the planned route. ### Claim 5 (depends on 1) The computer-implemented method claim 1, wherein the group of objects comprises one or more of: construction objects in a construction zone of a roadway; foreign object debris in the roadway; parked emergency vehicles in the roadway; or an unknown class object in close proximity to the autonomous vehicle. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein an original classification of each object included in the group of objects is stored along with the classification update for each object included in the group of objects. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the method further comprises: providing, by the computing system, data indicative of the plurality of objects for display, wherein the data indicative of the plurality of objects comprises data indicative of the problem object along with the one or more other objects in proximity to the problem object within the plurality of objects. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein determining, by the computing system, the group of objects of the plurality of objects comprises receiving a first user input selection of the problem object and one or more of the other objects in proximity to the problem object from the plurality of objects displayed; and wherein determining, by the computing system, the classification update to be applied to the group of objects comprises receiving a second user input comprising an indication of the classification update to be applied to the group of objects. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein the one or more other objects in proximity to the problem object are selected based at least in part on one or more predefined rules. ### Claim 10 (depends on 8) The computer-implemented method of claim 8, wherein the method further comprises: in response to receiving an indication of the classification update to be applied to the group of objects, providing, by the computing system, a request for confirmation of the classification update to be applied to the group of objects; and receiving, by the computing system, data indicating confirmation of the classification update to be applied to the group of objects before applying the classification update to the group of objects. ### Claim 11 (depends on 1) The computer-implemented method of claim 1, wherein the method further comprises: providing, by the computing system, the plurality of objects to a model that is configured to determine the group of objects comprising the problem object from the plurality of objects and determine the classification update for the group of objects. ### Claim 12 A computing system, comprising: one or more processors; and one or more memories including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: obtaining, from an autonomous vehicle, data indicative of a plurality of objects within a surrounding environment of the autonomous vehicle, the plurality of objects including at least at one problem object encountered by the autonomous vehicle while navigating a planned route; determining a group of objects of the plurality of objects, the group of objects including the problem object and one or more other objects in proximity to the problem object; determining a classification update to be applied to the group of objects; applying the classification update to the group of objects; and providing data indicative of the classification update for the group of objects to the autonomous vehicle for use in motion planning. ### Claim 13 (depends on 12) The computing system of claim 12, wherein the problem object has been detected by the autonomous vehicle with incorrect object classification data. ### Claim 14 (depends on 12) The computing system of claim 12, wherein the classification update to be applied to the group of objects comprises: an indication that the group of objects are static and the autonomous vehicle should pass on the left of the group of objects; an indication that the group of objects are static and the autonomous vehicle should pass on the right of the group of objects; or an indication that the group of objects are not static objects. ### Claim 15 (depends on 12) The computing system of claim 12, wherein the group of objects comprises one or more of: construction objects in a construction zone of a roadway; foreign object debris in the roadway; parked emergency vehicles in the roadway; or an unknown class object in close proximity to the autonomous vehicle. ### Claim 16 (depends on 12) The computing system of claim 12, wherein an original classification of each object included in the group of objects is stored along with the classification update for each object included in the group of objects. ### Claim 17 (depends on 12) The computing system of claim 12, wherein determining the group of objects of the plurality of objects, the group of objects including the problem object and one or more other objects in proximity to the problem object comprises receiving a selection of the problem object and one or more of the other objects in proximity to the problem object from a display of the plurality of objects; and wherein determining the classification update to be applied to the group of objects comprises receiving an indication of the classification update to be applied to the selected group of objects. ### Claim 18 (depends on 12) The computing system of claim 12, wherein the operations further comprise: providing the plurality of objects to a model that is configured to determine the group of objects inclusive of the problem object from the plurality of objects and to determine the classification update for the group of objects. ### Claim 19 One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising: obtaining, from an autonomous vehicle, data indicative of a plurality of objects within a surrounding environment of the autonomous vehicle, the plurality of objects including at least at one problem object encountered by the autonomous vehicle while navigating a planned route; determining a group of objects of the plurality of objects, the group of objects including the problem object and one or more other objects in proximity to the problem object; determining a classification update to be applied to the group of objects; applying the classification update to the group of objects; and providing data indicative of the classification update for the group of objects to the autonomous vehicle for use in motion planning. ### Claim 20 (depends on 19) The one or more tangible, non-transitory computer-readable media of claim 19, wherein the classification update to be applied to the group of objects comprises: an indication that the group of objects are static and the autonomous vehicle should pass on the left of the group of objects; an indication that the group of objects are static and the autonomous vehicle should pass on the right of the group of objects; or an indication that the group of objects are not static objects.