Traffic jam patterns can be identified and, based on historical traffic data, pre-traffic jam patterns that are likely to result in the traffic jam patterns can be identified as well. Real-time traffic data regarding a driving road of a community can be received and analyzed to determine whether the real-time traffic data match with a pre-traffic jam pattern. If the data matches a pre-traffic jam pattern, an alerting signal for predicting a traffic jam can be transmitted.

Aspects of the disclosure provide for the generation of a service area map for autonomous vehicles. For instance, graph nodes of a road network may be iterated through in order to identify a set of reachable graph nodes based on a set of routing parameters that define driving limits for the autonomous vehicles. The road network may include the graph nodes as well as edges connecting ones of the graph nodes. A set of S2 cells may be identified based on the set of reachable graph nodes. Vertices of each S2 cell of the set of S2 cells may be determined based on whether each S2 cell of the set of S2 cells is occupied by any of the graph nodes of the set of reachable graph nodes. Contours through cells may be drawn based on the scores. The service area map may be generated using the contours.

A method of determining a set of association grids, an electronic device, and a storage medium are provided, which relate to a field of computer technology, in particular to fields of Internet of Things, Internet of Vehicles, and big data. A specific implementation includes: gridding a map to obtain a set of candidate grids; selecting a starting grid from the set of candidate grids; performing, based on the starting grid, a filtering operation for determining an existence or a non-existence of a geographic association relationship on the set of candidate grids, to obtain an association grid; repeatedly performing, in response to a determination that the association grid does not meet a condition, the filtering operation by using the association grid, until an obtained association grid meets the condition; and merging the association grid obtained by each filtering operation with the starting grid to obtain a set of association grids.

A method is provided for integrally specifying a geographic location and an indoor location within a building. When the coordinates of one point on the Earth are given as a geodetic latitude φ, a longitude λ, and an ellipsoidal height h in a geodetic coordinate system based on the Earth ellipsoid, the location of the point is represented with a new coordinates including a Northing N, an Easting E, and selectively a floor representing integer F. The Northing N is given as a linear function of the distance measured along the prime meridian from the latitude-longitude origin to the waypoint, and the Easting is given as a linear function of the distance measured along the parallel of latitude from the waypoint to the ellipsoidal point.

A system may be configured to perform label recollection, e.g., by automatically snapping, via a trained ML model, a set of vector labels by aligning one or more of the labels to an image, the alignment being performed at a quality that satisfies a criterion. Before this automatic snapping or matching of vectorized labels with reference imagery, this ML model may obtain training data from an output of another trained ML model. In another context, a computer-implemented method is disclosed for creating training data that better aligns labels with corresponding image features. This training data, created with reduced effort yet increased quality, may then be fed into to existing models, resulting in an automated pipeline.

A method for recording data of a trip is implemented by a processor of a recording device and includes: in response to receipt of a location record, performing a trajectory analysis on the location record so as to determine, with respect to a plurality of point of interest (POI), whether each of the POIs has been visited; and generating a travel record that includes the location record and any POI that has been visited.

Systems and methods for projecting a three-dimensional (3D) surface to a two-dimensional (2D) surface for use in autonomous driving are disclosed. In one aspect, a control system for an autonomous vehicle includes a processor and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to: receive a 3D map including a plurality of objects, determine a base point in the 3D map, shift the objects in the 3D map based on the base point, project the objects in the shifted 3D map to a 2D map, and output the 2D map.

A route generation device and a travel control device for suppressing a target route of a vehicle to be discontinuous before and after starting a lane change are obtained. A position obtaining unit obtains location information of the vehicle. A first route extraction unit extracts the target route as first points in sequence assuming that the vehicle continues to travel in the lane in which the vehicle is traveling before starting the lane change. The second route extraction unit extracts the target route a target route as second points in sequence assuming that the vehicle is currently traveling in an adjacent lane in which the vehicle travels after completing the lane change, and the vehicle continues to travel in the adjacent lane. The route calculation unit calculates the third points representing the target route.

Enclosed are embodiments of an ML-based framework for drivable surface annotation. In an embodiment, a method comprises: obtaining, using at least one processor, multimodal map data for a geographic region; and automatically annotating, using the at least one processor, one or more semantic masks of the map data using a machine learning model.

Provided are a map building method, a navigation method, a device and a system. A detected obstacle is identified, and a type of the obstacle is determined according to an identification result from multiple obstacle types obtained through classification according to an obstacle characteristic; a map is built and the obstacle is marked, and the type of the obstacle is recorded. A newly added obstacle detected on a path is identified during navigation, and obstacle avoidance process is performed according to the type of the newly added obstacle.