A method of updating a road information, an electronic device, and a storage medium, which relate to an artificial intelligence technology field, in particular to fields of computer vision, deep learning, big data, high-definition map, intelligent transportation, automatic driving and autonomous parking, cloud service, Internet of Vehicles and intelligent cabin technologies. The method includes: processing image data corresponding to a target road region to obtain a set of first road lines; obtaining a set of second road lines according to a trajectory map corresponding to the target road region; calibrating the set of first road lines by using the set of second road lines to obtain a set of third road lines; combining the set of third road lines and a set of historical road lines corresponding to the target road region to obtain a combination result; and updating the set of historical road lines according to the combination result.

A method is provided for generating and revising map geometry based on a received image and probe data. A method may include: receiving probe data from a first period of time, where the probe data from a first period of time is from a plurality of probes within a predefined geographic region; generating a first image of the predefined geographic region based on the probe data from the first period of time; receiving probe data from a second period of time different from the first period of time, where the probe data from the second period of time is from a plurality of probes within the predefined geographic region; generating a second image based on the probe data from the second period of time; comparing the first image to the second image; and generating a revised route geometry based on changes detected between the first image and the second image.

The present disclosure provides a method for optimizing map data, a device and a storage medium, and in particular relates to artificial intelligence, intelligent transportation, smart cities and smart cockpits. A specific implementation is: performing a completeness detection of a lane line on received local map data comprising road condition information to obtain a detection result, the detection result comprising the lane line being complete or is the lane line having a missing area; in a case where the detection result is the lane line having the missing area, performing completion processing on the lane line having the missing area to obtain completed local map data; and performing a rationality detection on the completed local map data, and in a case of passing the detection, synthesizing the completed local map data with global map data to obtain an optimization result of map data.

A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a roadgraph, the roadgraph including an autonomous vehicle driving path, modify the roadgraph to obtain a modified roadgraph by adjusting a trajectory of the autonomous vehicle driving path, place a set of artifacts along one or more lane boundaries of the modified roadgraph to generate a synthetic scene, and train a machine learning model used to navigate an autonomous vehicle based on the synthetic scene.

An information processing device (100) includes a map information generation unit (11) and a usage amount calculation unit (12). The map information generation unit (11) generates map information by using measurement information including a plurality of types of information measured by a measurement device (300) which measures information on features and is mounted on a measurement vehicle (310). The usage amount calculation unit (12) calculates a usage amount of the map information generation unit (11) used for generating the map information by using at least one of the plurality of types of information included in measurement information.

A map generation device (10) generates linearization information expressing at least one or the other of a marking line of a roadway and a road shoulder edge based on measurement information of a periphery of the roadway. The measurement information is obtained by a measurement device. The map generation device (10) calculates an evaluation value expressing a reliability degree of partial information, for each partial information constituting the linearization information. A map editing device (20) displays the partial information in different modes according to the evaluation value, thereby displaying the linearization information. The map editing device (20) accepts input of editing information for the displayed linearization information.

A centerline correction apparatus according to one embodiment corrects a centerline of a path that is acquired from shape information about an indoor space or road shape information about an outdoor space and that is a travelable region in the indoor space or the outdoor space. This centerline correction apparatus includes a centerline correction unit that performs: a center point extraction process that extracts a center point of an individual side of the centerline; a centerline correction process that moves the centerline to be at a center with respect to a distance to a boundary of an original shape based on the extracted center point; and an unnecessary centerline deletion process that deletes an unnecessary one of the moved centerlines.

According to an aspect of an embodiment, operations may comprise obtaining a pose graph that comprises a plurality of nodes. The operations may also comprise dividing the pose graph into a plurality of pose subgraphs, each pose subgraph comprising one or more respective pose subgraph interior nodes and one or more respective pose subgraph boundary nodes. The operations may also comprise generating one or more boundary subgraphs based on the plurality of pose subgraphs, each of the one or more boundary subgraphs comprising one or more respective boundary subgraph boundary nodes and comprising one or more respective boundary subgraph interior nodes. The operations may also comprise obtaining an optimized pose graph by performing a pose graph optimization. The pose graph optimization may comprise performing a pose subgraph optimization of the plurality of pose subgraphs and performing a boundary subgraph optimization of the plurality of boundary subgraphs.

In one aspect, a vehicle localization system implements the following steps: receiving a predetermined road map; receiving at least one captured image from an image capture device of a vehicle; processing, by a road detection component, the at least one captured image, to identify therein road structure for matching with corresponding structure of the predetermined road map, and determine a location of the vehicle relative to the identified road structure; and using the determined location of the vehicle relative to the identified road structure to determine a location of the vehicle on the road map, by matching the road structure identified in the at least one captured image with the corresponding road structure of the predetermined road map.

A vehicle, system and method of navigating the vehicle. The system includes a sensor and a processor. The sensor is configured to obtain a first set of detection points representative of a lane of a road section at a first time step and a second set of detection points representative of the lane at a second time step. The processor is configured to determine a set of predicted points for the second time step from the first set of detection points, obtain a set of fused points from the second set of detection points and the set of predicted points, and navigate the vehicle using the set of fused points.