# US 11,113,969 B2 · Data-to-camera (D2C) based filters for improved object detection in images based on vehicle-to-everything communication > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,113,969 B2 - Document ID: US11113969B2 - Status: Granted - Kind: B2 - Application number: 16/295,583 - Filed: 2019-03-07 - Published: 2020-09-10 - Granted: 2021-09-07 - Earliest claimed: 2019-03-07 - Applicants: Toyota Jidosha Kabushiki Kaisha - Inventors: Sergei Avedisov; Gaurav Bansal; Hongsheng Lu; Rui Guo - Web page: https://patentgrep.com/patent/US11113969B2 ## CPC - G08G 1/096716: {where the received information does not generate an automatic action on the vehicle control} - G06F 18/2113: by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G08G 1/09675: {where a selection from the received information takes place in the vehicle} - G08G 1/096775: {where the origin of the information is a central station} - G08G 1/16: Anti-collision systems (road vehicle drive control systems for predicting or avoiding probable or impending collision otherwise than by control of a particular sub-unit B60W30/08) - H04W 4/40: for vehicles, e.g. vehicle-to-pedestrians [V2P] - B60R 2300/30: characterised by the type of image processing - G08G 1/164: {Centralised systems, e.g. external to vehicles} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - 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}) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - B60R: VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR ## Abstract The disclosure describes a method for an ego vehicle. The method includes receiving a vehicle-to-everything (V2X) message that describes an object that is within proximity of an ego vehicle. The method further includes generating a set of data-to-camera (D2C) filters that are specific to the object described by the V2X message. The method further includes applying the set of D2C filters to image data that describes an initial image of the object. The method further includes generating a modified image, based on applying the set of D2C filters to the image data, wherein the modified image includes an indication of (1) a location and a size of the object in the initial image and (2) a type of object in the initial image. ## Claims ### Claim 1 A method comprising: receiving a vehicle-to-everything (V2X) message that describes an object that is within proximity of an ego vehicle; generating a set of data-to-camera (D2C) filters that are specific to the object described by the V2X message by: determining where the object is expected to appear in an initial image, generating a first D2C filter for an object type, and generating a second D2C filter for an object location based on where the object is expected to appear in the initial image; applying the set of D2C filters to image data that describes the initial image of the object; and generating a modified image, based on applying the set of D2C filters to the image data, wherein the modified image includes an indication of (1) a location and a size of the object in the initial image and (2) a type of object in the initial image. ### Claim 2 (depends on 1) The method of claim 1, further comprising: providing the modified image to an Advanced Driver Assistance System of the ego vehicle to reduce or eliminate a driving hazard caused by the object. ### Claim 3 (depends on 1) The method of claim 1, wherein the V2X message includes a Basic Safety Message (BSM) that describes the location of the object, the size of the object, an identity of the object, and a heading of the object. ### Claim 4 (depends on 1) The method of claim 1, wherein the D2C filters are generated using a trained machine-learning model. ### Claim 5 (depends on 1) The method of claim 1, further comprising: receiving range data describing a range between the object and the ego vehicle, wherein the range is measured by a range finding sensor of the ego vehicle. ### Claim 6 (depends on 1) The method of claim 1, wherein the object is a remote vehicle and the V2X message is a Basic Safety Message (BSM) received from the remote vehicle. ### Claim 7 (depends on 1) The method of claim 1, wherein the set of D2C filters are generated in real time and the set of D2C filters describe digital data that is operable to modify the initial image. ### Claim 8 (depends on 1) The method of claim 1, wherein the modified image further includes a bounding box that surrounds the object. ### Claim 9 A system for an ego vehicle, comprising: a processor; and a non-transitory memory storing computer code which, when executed by the processor, causes the processor to: receive a vehicle-to-everything (V2X) message that describes an object that is within proximity of an ego vehicle; generate a set of data-to-camera (D2C) filters that are specific to the object described by the V2X message by: determining where the object is expected to appear in an initial image, generating a first D2C filter for an object type, and generating a second D2C filter for an object location based on where the object is expected to appear in the initial image; apply the set of D2C filters to image data that describes an initial image of the object; and generate a modified image, based on applying the set of D2C filters to the image data, wherein the modified image includes an indication of (1) a location and a size of the object in the initial image and (2) a type of object in the initial image. ### Claim 10 (depends on 9) The system of claim 9, wherein the computer code, when executed by the processor, further causes the processor to: provide the modified image to an Advanced Driver Assistance System of the ego vehicle to reduce or eliminate a driving hazard caused by the object. ### Claim 11 (depends on 9) The system of claim 9, wherein the V2X message includes a Basic Safety Message (BSM) that describes the location of the object, the size of the object, an identity of the object, and a heading of the object. ### Claim 12 (depends on 9) The system of claim 9, wherein the D2C filters are generated using a trained machine-learning model. ### Claim 13 (depends on 9) The system of claim 9, wherein the computer code, when executed by the processor, further causes the processor to: receive range data describing a range between the object and the ego vehicle, wherein the range is measured by a range finding sensor of the ego vehicle. ### Claim 14 (depends on 9) The system of claim 9, wherein the object is a remote vehicle and the V2X message is a Basic Safety Message (BSM) received from the remote vehicle. ### Claim 15 A computer program product comprising a non-transitory memory storing computer-executable code that, when executed by a processor, causes the processor to: receive a vehicle-to-everything (V2X) message that describes an object that is within proximity of an ego vehicle; generate a set of data-to-camera (D2C) filters that are specific to the object described by the V2X message by: determining where the object is expected to appear in an initial image, generating a first D2C filter for an object type, and generating a second D2C filter for an object location based on where the object is expected to appear in the initial image; apply the set of D2C filters to image data that describes an initial image of the object; and generate a modified image, based on applying the set of D2C filters to the image data, wherein the modified image includes an indication of (1) a location and a size of the object in the initial image and (2) a type of object in the initial image. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the computer-executable code further causes the processor to: provide the modified image to an Advanced Driver Assistance System of the ego vehicle to reduce or eliminate a driving hazard caused by the object. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the V2X message includes a Basic Safety Message (BSM) that describes the location of the object, the size of the object, an identity of the object, and a heading of the object. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein the D2C filters are generated using a trained machine-learning model. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the computer-executable code further causes the processor to: receive range data describing a range between the object and the ego vehicle, wherein the range is measured by a range finding sensor of the ego vehicle. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein the object is a remote vehicle and the V2X message is a Basic Safety Message (BSM) received from the remote vehicle.