# US 2025/0148799 A1 · Apparatus and method for controlling a vehicle > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0148799 A1 - Document ID: US20250148799A1 - Status: Published application - Kind: A1 - Application number: 18/602,282 - Filed: 2024-03-12 - Published: 2025-05-08 - Earliest claimed: 2023-11-06 - Assignees: Hyundai Motor Company; Kia Corporation - Applicants: Hyundai Motor Company; Kia Corporation - Inventors: Ji Hee Han; Jung Phil Kwon - Web page: https://patentgrep.com/patent/US20250148799A1 ## CPC - 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 - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06T 7/277: involving stochastic approaches, e.g. using Kalman filters - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06T 2207/30252: Vehicle exterior; Vicinity of vehicle - G06V 2201/08: Detecting or categorising vehicles - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract An apparatus for controlling a vehicle includes a sensor having at least one camera to obtain information about objects positioned around the vehicle. The apparatus also includes a controller configured to detect at least one object image from an image obtained from the camera. The controller predicts a position of each object on the image currently obtained, based on information about objects recognized from an image previously obtained. The controller recognizes an object by analyzing correlation between two objects based on an object image detected at the predicted position and an object image previously recognized. ## Claims ### Claim 1 An apparatus for controlling a vehicle, the apparatus comprising: a sensor including at least one camera to obtain information about objects positioned around the vehicle; and a controller configured to detect at least one object image from an image obtained from the camera, predict a position of each object on the image currently obtained, based on information about objects recognized from an image previously obtained, recognize an object by analyzing correlation between two objects based on an object image detected at the predicted position and an object image previously recognized, extract a first image, which corresponds to a front surface or a rear surface, of the object image, extract a second image, which corresponds to a side surface, and analyze the correlation between the two objects by comparing a feature value of the first image and a feature value of the second image, with a feature value of a third image corresponding to the front surface or the rear surface and a feature value of a fourth image, respectively, wherein the third image and the fourth image are extracted from the object image previously recognized. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the controller is further configured to: calculate a distance value between the feature value of the first image and the feature value of the third image; calculate a distance value between the feature value of the second image and the feature value of the fourth image; and analyze the correlation between the two objects, based on the calculated distance values. ### Claim 3 (depends on 2) The apparatus of claim 2, wherein the controller is further configured to calculate a Mahalanobis distance between the feature values and analyze the correlation between the two objects. ### Claim 4 (depends on 2) The apparatus of claim 2, wherein the controller is further configured to calculate a cosine distance between the feature values and analyze the correlation between the two objects. ### Claim 5 (depends on 2) The apparatus of claim 2, wherein the controller is further configured to: calculate a proportion of a region, which is occupied by each of the first image and the second image, in a bounding box, which is defined as a rectangular region including the object image, based on the bounding box; and analyze reliability for each of the first image and the second image, based on the calculated proportion. ### Claim 6 (depends on 5) The apparatus of claim 5, wherein the controller is further configured to analyze the correlation between the two objects, based on a feature value of at least one of the first image and the second image, in response to a determination that reliability of the at least one of the first image or the second image exceeds a reference value. ### Claim 7 (depends on 5) The apparatus of claim 5, wherein the controller is further configured to analyze the correlation between the two objects, based on a distance value calculated based on a distance matrix of learning data, which is previously learned, when reliability for at least one of the first image or the second image is equal to or less than a reference value. ### Claim 8 (depends on 1) The apparatus of claim 1, wherein the controller is further configured to: calculate reliability for a front surface image or a rear surface image and for a side surface image detected from one image of images input from at least two cameras; calculate reliability for a front surface image or a rear surface image and for a side surface image detected from another image of the images input from the at least two cameras, when the images are input from the at least two cameras; and analyze the correlation between the two objects in the two images, based on a weighted sum calculated based on the calculated reliability. ### Claim 9 (depends on 1) The apparatus of claim 1, wherein the controller is further configured to: analyze the correlation between the two objects, based on the first image and the second image; learn an operation for recognizing the same object; and recognize the object by reflecting learning data in inputting a next image from the camera. ### Claim 10 (depends on 1) The apparatus of claim 1, wherein the controller is further configured to allocate a tracking identity (ID) to an object recognized from the object image, wherein the tacking ID is the same as a tracking ID of an object previously recognized, when the object recognized from the object image has the correlation with the object previously recognized. ### Claim 11 A method for controlling a vehicle, the method comprising: obtaining an image about objects positioned around the vehicle from at least one camera; detecting at least one object image from the image obtained to predict a position of each object on the image currently obtained, based on information about objects recognized from an image previously obtained; and recognizing an object by analyzing correlation between two objects based on an object image detected at the predicted position and an object image previously recognized, wherein the recognizing of the object comprises extracting a first image, which corresponds to a front surface or a rear surface, of the object image, extracting a second image, which corresponds to a side surface, analyzing the correlation between the two objects by comparing a feature value of the first image and a feature value of the second image, with a feature value of a third image corresponding to the front surface or the rear surface and a feature value of a fourth image, respectively, wherein the third image and the fourth image are extracted from the object image previously recognized. ### Claim 12 (depends on 11) The method of claim 11, wherein analyzing the correlation between the two objects comprises: calculating a distance value between the feature value of the first image and the feature value of the third image; calculating a distance value between the feature value of the second image and the feature value of the fourth image; and analyzing the correlation between the two objects, based on the calculated distance values. ### Claim 13 (depends on 12) The method of claim 12, wherein analyzing the correlation between the two objects comprises calculating a Mahalanobis distance between the feature values. ### Claim 14 (depends on 12) The method of claim 12, wherein analyzing the correlation between the two objects comprises calculating a cosine distance between the feature values. ### Claim 15 (depends on 14) The method of claim 14, wherein analyzing the correlation between the two objects comprises: calculating a proportion of a region, which is occupied by each of the first image and the second image, in a bounding box, which is defined as a rectangular region including the object image, based on the bounding box; and analyzing reliability for each of the first image and the second image, based on the calculated proportion. ### Claim 16 (depends on 12) The method of claim 12, wherein analyzing the correlation between the two objects comprises: calculating a proportion of a region, which is occupied by each of the first image and the second image, in a bounding box, which is defined as a rectangular region including the object image, based on the bounding box; and analyzing reliability for each of the first image and the second image, based on the calculated proportion. ### Claim 17 (depends on 16) The method of claim 16, wherein analyzing the correlation between the two objects comprises analyzing the correlation between the two objects, based on a feature value of at least one of the first image and the second image, in response to a determination that reliability of the at least one of the first image or the second image exceeds a reference value. ### Claim 18 (depends on 16) The method of claim 16, wherein analyzing the correlation between the two objects comprises analyzing the correlation between the two objects, based on a distance value calculated based on a distance matrix of learning data, which is previously learned, when reliability for at least one of the first image or the second image is equal to or less than a reference value. ### Claim 19 (depends on 11) The method of claim 11, wherein recognizing the object comprises: calculating reliability for a front surface image or a rear surface image and for a side surface image detected from one image of images input from at least two cameras; calculating reliability for a front surface image or a rear surface image and for a side surface image detected from another image of the images input from the at least two cameras, when the images are input from the at least two cameras; and analyzing the correlation between the two objects in the two images, based on a weighted sum calculated based on the calculated reliability. ### Claim 20 (depends on 11) The method of claim 11, wherein recognizing the object further comprises allocating a tracking identity (ID) to an object recognized from the object image, wherein the tacking ID is the same as a tracking ID of an object previously recognized, when the object recognized from the object image has the correlation with the object previously recognized.