In various examples, a high definition (HD) map is provided that includes a segmented data structure that allows for selective access to desired road segments and corresponding layers of map data. For example, the HD map may be segmented into a series of tiles that may correspond to a geographic region, and each of the tiles may include any number of road segments corresponding to portions of the geographic region. Each road segment may include a corresponding set of layers—which may include driving layers for use by the ego-machine and/or training layers for generating ground truth data—from the HD map that are associated with the road segment alone. As such, when traversing the environment, an ego-machine may determine one or more road segments within a tile corresponding to a current location, and may selectively download one or more layers for each of the one or more road segments.

A method for providing map data for at least two types of zones includes providing first map data for two types of zones, wherein the zones are described by regions; providing second map data for one of the types of zones, wherein the type is described by the listing of road sections; providing a map which contains all of the road sections and the positions thereof; verifying, by way of the map, whether the regions indicated in the first map data for the one of the types of zones correspond with the road sections indicated in the second map data for the one of the types of zones; in the event that they do not correspond, modifying the zones of the one of the types of zones in the first map data; and providing the first map data, corrected as applicable.

In various examples, health of a high definition (HD) map may be monitored to determine whether inaccuracies exist in one or more layers of the HD map. For example, as one or more vehicles rely on the HD map to traverse portions of an environment, disagreements between perception of the one or more vehicles, map layers of the HD map, and/or other disagreement types may be identified and aggregated. Where errors are identified that indicate a drop in health of the HD map, updated data may be crowdsourced from one or more vehicles corresponding to a location of disagreement within the HD map, and the updated data may be used to update, verify, and validate the HD map.

Disclosed embodiments are related to techniques for implementing Collective Perception Services in Intelligent Transport Systems. Embodiments include technologies for sharing layered costmaps and/or sharing perceived object clusters in Collective Perception Messages. Other embodiments may be described and/or claimed.

Systems and methods for aggregating and integrating distributed grid element inputs are disclosed. A data platform is provided for a distribution power grid. The data platform provides a crowd-sourced gaming system for identifying grid elements and determining dynamic electric power topology. The data platform also provides an interactive interface for displaying a view of a certain area with identified grid elements. The data platform communicatively connects to the identified grid elements, collects data from the identified grid elements, and manages the distribution power grid.

In an autonomous driving robot control system using a non-high definition map and a high definition map which is capable of operating a robot even in an area where the high definition map is not acquired, and a method of driving the same, the system includes: a control system; and a robot controlled according to an instruction from the control system, wherein the robot searches for a travel route based on the high definition map in a high definition map acquired area, and the robot searches for a travel route based on the non-high definition map in a high definition map unacquired area.

The present disclosure is related to generating map data explicitly indicating a total drivable surface, which may include multiple types of drivable surfaces. For instance, a given portion of a map may include map data indicating a combination of various drivable surfaces, such as road segments, lane properties, intersections, parking areas, shoulders, driveways, etc. Examples of the present disclosure join these different types of drivable surfaces into combined map data that explicitly indicates a total drivable surface, such as a perimeter boundary indicating or representing a transition from a drivable surface to a non-drivable surface. The map data indicating the total drivable surface may be searched to determine information related to a drivable surface boundary, such as location and type. This boundary information may be used in various contexts, such as when planning a trajectory or remotely controlling a vehicle.

Embodiments of this disclosure discloses a map data processing method, apparatus, computer device and storage medium, the method including: outputting navigation route information associated with a navigation user in a navigation interface of a map client application, the navigation interface including map layer information, the navigation route information being superimposed and displayed on the map layer information, and map field information of the map layer information being first field information; obtaining location information of the navigation user on the navigation route information; and updating and displaying the map field information from the first field information to second field information in the navigation interface when the location information is monitored to be in an intersection area on the navigation route information. By the embodiments of this disclosure, the safety during navigation may be improved.

Disclosed herein is a technique for processing map change data in a map client that uses the map changes to apply changes and updates to current map data. The map client provides the updated map data to other components in an autonomous vehicle. The map change data describes the processing in the map client to obtain updated map data from the map data.

In one embodiment, a method includes a computing system collecting first sensor data, of a particular geographic location, from a first type of sensor. The computing system may process the first sensor data to identify one or more first objects at the particular geographic location. The computing system may access an existing high-definition (HD) map associated with the particular geographic location. The existing HD map includes one or more second objects and is generating using second sensor data collected with a second type of sensor. The computing system may determine whether the one or more first objects are included in the existing HD map. In response to determining that the one or more first objects are not included in the existing HD map, the computing system may update the existing HD map to generate an updated HD map that includes the first objects and the second objects.