Provided is a sensor information processing device that processes the detection results of the plurality of external-environment sensors that recognize the lane marker that divides the lane and can identify the lane marker more accurately than before. A sensor information processing device 100 includes a storage device 102 that stores a past detection result De of an external-environment sensor 200 as time-series data, and a central processing unit 101 that identifies a lane marker on the basis of the time-series data. The central processing unit 101 determines that the new detection result De belongs to the existing lane marker or the new lane marker on the basis of the comparison between the new detection result De not included in the time-series data and the time-series data.

A point cloud display method includes determining, from a first point cloud, points describing a target object, where the first point cloud describes a surrounding area of a vehicle in which the in-vehicle system is located, and the target object is to be identified by the in-vehicle system, generating a second point cloud based on the points, and displaying the second point cloud.

A system for navigating a host vehicle includes at least one electronic horizon processor to detect a map origin change event, and in response to a detected map origin change event, determine an updated map origin and send to one or more navigation system processors a notification indicative of a change from an initial map origin to the updated map origin.

A system for navigating a host vehicle includes at least one electronic horizon processor to access a map representative of at least a road segment on which the host vehicle travels or is expected to travel, wherein the map includes one or more splines representative of road features associated with the road segment, localize the host vehicle relative to a drivable path for the host vehicle represented among the one or more splines, determine a set of points associated with the one or more splines based on the localization of the host vehicle relative to the drivable path for the host vehicle, and generate a navigation information packet including information associated with the one or more splines and the determined set of points relative to the one or more splines.

A system for navigating a host vehicle includes at least one electronic horizon processor to determine an electronic horizon for the host vehicle based on localization of the host vehicle relative to at least one mapped feature, determine a set of internode road sub-segments that are included in the electronic horizon, generate one or more navigation information packets including information associated with the set of internode road sub-segments included in the electronic horizon, and output the generated one or more navigation information packets to one or more navigation system processors configured to cause the host vehicle to execute at least one navigational maneuver based on the information included in the navigation information packet.

A visual localization support system is provided. The visual localization support system includes one or more guidance indicators place on a road surface of a roadway, wherein the one or more guidance indicators each include a matrix barcode that uniquely identifies a location by latitude, longitude, and altitude, and describes an affine shape of the guidance indicator.

A road slope predicting method, a device, and a storage medium are disclosed. The road slope predicting method includes obtaining a road image of a road by a camera, detecting a first image lane line and a second image lane line of the road from the road image, setting a number of road image segmentation points along a road image center line of the first image lane line and the second image lane line respectively by means of the corresponding road image segmentation points, determining the pitch angle of the camera with respect to the road plane at each road space segmentation point; based on the internal and external parameters of the camera and the pitch angle, a space coordinate of each road space segmentation points is calculated in a recursive man. A road model of the road is constructed based on each space coordinate.

An external parameter calibration method for an image acquisition apparatus is disclosed. The method includes acquiring images from images acquired by the image acquisition apparatus. The images contain reference objects acquired by the image acquisition apparatus during the driving of the vehicle. The reference objects in the images are divided into a number of sections along a road direction in which the vehicle is located, and reference objects in each of the sections are fitted into straight lines. Pitch angles and yaw angles of the image acquisition apparatus are determined based on vanishing points of a straight line in each of the sections. The sequences of the determined pitch and yaw angles are filtered. Straight portions in the road from the filtered sequences of pitch and yaw angles are obtained. Data of the pitch angles and yaw angles corresponding to the straight portions are stored to a data stack.

An image processing apparatus includes: a determiner that determines whether or not a vehicle in which a camera is mounted is located at a specific place where a traveling direction of the vehicle changes, the camera picking up an image of an entire image pickup range with first image quality and picking up an image of a partial region in the image pickup range with second image quality that is higher image quality than the first image quality; and a region changer that, when it is determined that the vehicle is located at the specific place, changes a position of the partial region in the image pickup range such that an image of a region of attention that is included in the image pickup range and an image of which is to be picked up with the second image quality is picked up.

In various examples, operations include obtaining, from a machine learning model, feature classifications that correspond to features of objects depicted in images of a geographical area in which the images are provided to the machine learning model. The operations may also include annotating the images with three-dimensional representations that are based on the obtained feature classifications. Further, the operations may include generating map data corresponding to the geographical area based on the annotated images.