A method detects a lane for a transverse guidance of a vehicle. The transverse guidance of the vehicle is based on a roadway model. The method has the steps of ascertaining one or more features which are suitable for influencing the detection of the lane; detecting a lane on the basis of a sensor system of the vehicle; and ascertaining the roadway model on the basis of the detected lane and the ascertained one or more features. The method optionally has the steps of additionally receiving navigation data and transversely guiding the vehicle on the basis of the ascertained roadway model.

A navigation service generates a route for navigating from a route origin to a route destination using a private roads repository. A ghost road is along the route, and a ghost origin and a ghost destination of the ghost road is identified. Using an application programming interface of a base roads engine, a request for a route is sent from the ghost origin to the ghost destination. From the base roads engine and in response to the request, a replacement section from the ghost origin to the ghost destination is received. The ghost road is replaced with the replacement section to create an updated route, which is presented.

Communications server apparatus (102) for generating navigable map data comprises a processor (116) and a memory (118), and is configured, under control of the processor, to execute instructions (120) stored in the memory: to generate a series (340) of journey trace data sets (338), each journey trace data set comprising data representing a user journey (310), each journey trace data set being derived using geolocation transmissions (318) from a communications device (104, 106) of a user (308) undertaking the user journey; to aggregate the series of journey trace data sets to generate route image data comprising data representing a network of navigable routes; and to generate, from the route image data, the navigable map data, the navigable map data comprising data representing a series of geolocations corresponding to the network of navigable routes.

A method and apparatus evaluates a map for autonomous driving. The method acquires pixel points of lane lines of a road in a static map; acquires pixel points of lane lines of the road in a real-time map; forms first fitted curves of the lane lines in the static map by fitting the acquired pixel points of the lane lines in the static map respectively; forms second fitted curves of the lane lines in the real-time map by fitting the acquired pixel points of the lane lines in the real-time map respectively; calculates fitted-curve-similarities between the first fitted curves and the corresponding second fitted curves; and evaluates the consistency of the static map and the real-time map based on the fitted-curve-similarities.

A method for generating a three-dimensional (3D) lane model, the method including calculating a free space indicating a driving-allowed area based on a driving image captured from a vehicle camera, generating a dominant plane indicating plane information of a road based on either or both of depth information of the free space and a depth map corresponding to a front of the vehicle, and generating a 3D short-distance road model based on the dominant plane.

A system and a method of generating a map for navigating a vehicle. The system includes a remote processor. The remote processor is configured to determine an anchor point for a location of a road segment based on data from at least one data source, place the anchor point within an aerial image of the road segment, predict a boundary marking of the road segment on the aerial image based on the anchor point, and provide the boundary marking to the vehicle. A vehicle processor navigates the vehicle along the road segment using the boundary marking.

A system, map server and method of navigating a vehicle through an intersection. The map server includes a remote processor and a communication device. The remote processor determines a first road edge for a first road entering an intersection and a second road edge for a second road entering the intersection. The remote processor constructs an intersection edge that connects a first point on the first road edge to a second point on the second road edge. The communication device communicates the intersection edge to the vehicle. A vehicle processor at the vehicle navigates the vehicle through the intersection using the intersection edge.

According to an embodiment, a surroundings recognition device includes an acquirer configured to acquire first road information including a road boundary near a host vehicle from map information on the basis of position information of the host vehicle, a recognizer configured to recognize a road boundary near the host vehicle on the basis of an output of an external environment sensor, and a determiner configured to determine that a first road boundary matches a second road boundary when the second road boundary included in second road information that is a recognition result of the recognizer is within a road boundary determination area based on a position of the first road boundary included in the first road information acquired by the acquirer. The determiner sets a size of the road boundary determination area on the basis of a predetermined condition.

A management device includes processing circuitry configured to receive a plurality of inputs of road map data, refer to the road map data and generate a first polygon representing a lane region, generate, for a spatial mesh divided into a predetermined size, a second polygon representing a spatial index, and determine in which spatial mesh of a plurality of spatial meshes the first polygon exists, and in accordance with a result of the determination, and store data on the first polygon and data on the second polygon in a road coordinate database in association with each other.

The present disclosure provides an intersection vertex height value acquisition method and apparatus, an electronic device and a storage medium, and relates to the field of artificial intelligence such as intelligent transportation. The method may include: determining roads with starting points or ending points located in a to-be-processed intersection; and performing, for any vertex in a polygonal intersection region corresponding to the intersection, the following processing: determining a road corresponding to the vertex, taking the determined road as a matching road of the vertex, and determining a height value of the vertex according to height values of endpoints on a road center line of the matching road.