A system and method of using surround view for calculating coordinates for localization of a vehicle includes continuously receiving optical data from an optical sensing system having one or more cameras, detecting ground truth data, and storing the ground truth data within a memory. Utilizing a path planner, the method calculates a first path from a first location to a second location different from the first location and moves the vehicle from the first to the second location along the first path. As the vehicle is moved, the method tracks a position of the second location relative to the vehicle and predicts a position of the vehicle relative to the second location based on a current operating state of the vehicle positioning systems and the first path. The method periodically adjusts the first path in response to the optical data as the vehicle moves between the first and second locations.

Embodiments are directed towards the interpretation of driver intent and communication of that intent with autonomous vehicles at a traffic intersection. A computing device that sits on the dashboard of a vehicle includes at least one camera, an output device, and circuitry. The computing device captures first images of the driver in the vehicle and second images of the traffic intersection. The computing device identifies another vehicle at or approaching the intersection based on an analysis of the second images. The computing device determines an attention direction and hand movement of the driver based on an analysis of the first images to determine a driving intent of the driver. The computing device provides information to the other vehicle indicating the driving intent of the driver. The computing device may also obtain the driving intent of the other vehicle and provide information to the driver indicating the other vehicle's intent.

The present disclosure relates to an indoor positioning method for operating an indoor positioning system and an indoor positioning apparatus by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for the Internet of Things. The indoor positioning method according to an embodiment of the present disclosure includes receiving map data and map information data of an indoor map in response to a presence of the indoor map of an indoor space, acquiring an image of the indoor space at a device camera, comparing image information of the indoor map with the acquired image information of the indoor space based on the map data and the map information data of the indoor space, and performing indoor localizing of the indoor space based on a result of the comparing.

An on-board vehicle system and method for camera or sensor-based parking spot detection and identification is provided. This system and method utilizes a standard front (or side or rear) camera or sensor image to detect and identify one or more parking spots at a distance via vector or like representation using a deep neural network trained with data annotated using an annotation tool, without first transforming the standard camera or sensor image(s) to a bird's-eye-view (BEV) or the like. The system and method can be incorporated in a driver-assist (DA) or autonomous driving (AD) system.

A neural network may be used to determine corner points of a skewed polygon (e.g., as displacement values to anchor box corner points) that accurately delineate a region in an image that defines a parking space. Further, the neural network may output confidence values predicting likelihoods that corner points of an anchor box correspond to an entrance to the parking spot. The confidence values may be used to select a subset of the corner points of the anchor box and/or skewed polygon in order to define the entrance to the parking spot. A minimum aggregate distance between corner points of a skewed polygon predicted using the CNN(s) and ground truth corner points of a parking spot may be used simplify a determination as to whether an anchor box should be used as a positive sample for training.

Techniques including receiving a distorted image from a camera disposed about a vehicle, detecting, in the distorted image, corner points associated with a target object, mapping the corner points to a distortion corrected domain based on one or more camera parameters, mapping the corner points and lines between the corner points back to a distorted domain based on the camera parameters, interpolating one or more intermediate points to generate lines between the corner points in the distortion corrected domain mapping the corner points and the lines between the corner points back to a distorted domain based on the camera parameters, and adjusting a direction of travel of the vehicle based on the located target object.

An adhered substance detection apparatus determines whether a substance is adhered to an image-capturing apparatus. The adhered substance detection apparatus includes a controller configured to function as: an extractor that extracts candidate regions for being an adhered substance region in which the substance is adhered to the image-capturing apparatus, the candidate regions being extracted based on an edge that is detected from pixels in an image captured by the image-capturing apparatus; and a final determiner that finally determines whether the candidate regions are the adhered substance region, based on i) an area of the candidate region located in a first region of the captured image, the first region including a road surface region in the captured image and ii) an area of the candidate region located in a second region of the captured image, the second region including a region other than the road surface region in the captured image.

A vision apparatus for a moving body is provided. The vision apparatus for a moving body includes a plurality of cameras that are arranged to be distanced from one another, and are arranged in a diagonal direction to the moving direction of the moving body, and a processor that receives images photographed at each of the plurality of cameras, and stereo-matches the plurality of received images and generates distance information for all directions of the moving body.

The technologies regarding dynamic image-based parking systems and methods of operation thereof are disclosed. An example method for operating a camera-based parking management system includes: activating an AI-enabled camera covering a parking area responsive to detecting a parking object; capturing a snapshot of the parking area using the AI-enabled camera; transmitting the snapshot to an edge processor to identify parking object(s) shown in the snapshot; determining that the parking object shown in the snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model; responsive to the determining: identifying first one or more characteristics about the parking object that potentially reduce identification accuracy; and identifying second one or more characteristics about the snapshot (e.g., imaging angle, orientation, resolution, network speed) that potentially reduce identification accuracy; and automatically adjusting one or more settings of the AI-enabled camera to capture a second snapshot.

Disclosed herein is a method for providing a parking lot guidance service of a server. The method includes: receiving parking lot data including information of a parking space in a parking lot from an image capturing apparatus for a vehicle provided in the vehicle; generating a parking lot model representing a real-time parking situation of the parking lot as an image based on the received parking lot data; and providing the parking guidance service to a user terminal apparatus using the generated parking lot model.