A system may include a display and a processor. The processor may be configured to: obtain aircraft data associated with an aircraft; obtain or generate storm top data, the storm top data including information associated with storm top altitudes and storm top locations; generate aircraft relative storm top data; generate an aircraft relative storm top image based at least on the aircraft relative storm top data, wherein the aircraft relative storm top image depicts a view of weather in front of the aircraft, wherein the aircraft relative storm top image conveys information associated with a difference between at least some of the storm top altitudes and an altitude of the aircraft; and output the aircraft relative storm top image as graphical data.

A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning the trajectories to generate aligned geospatial observations; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using one or more Set Transformers; generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations; and providing at least one of navigational assistance or at least semi-autonomous vehicle control based on the map geometries. According to some embodiments, aligning the trajectories includes applying a geospatial offset for one or more of the trajectories.

An approach is provided for identifying partitions associated with erratic pedestrian behaviors and their correlations to points of interest. For example, the approach involves receiving sensor data associated with a geographic area. The approach also involves based on the sensor data, determining pedestrian-behavior parameter(s) respectively for partition(s). Each respective partition of the partition(s) represents a respective subarea of the geographic area, a respective time period, or a combination thereof. The approach further involves identifying at least one erratic partition from the partition(s) based on determining that a respective pedestrian-behavior parameter associated with the at least one erratic partition deviates from a baseline pedestrian-behavior parameter by at least a threshold extent. The approach further involves determining a correlation of the at least one erratic partition to at least one map feature of a geographic database. The approach further involves providing the correlation as an output.

According to the present disclosure, a map matching apparatus includes a map information acquisition unit configured to acquire map information expressed by a plurality of links, a movement information acquisition unit configured to acquire movement information of a moving body on a predetermined route, and a matching unit configured to specify a link string, which is a string of the links corresponding to the route, based on the map information and the movement information, in which the matching unit is configured to generate a physical network, which is a road network, based on the connection relationship and the map information, generate a hierarchical logical network in which the physical network is duplicated in a plurality of layers and configured in a hierarchical manner, and specify the link string indicating a minimized cost in the hierarchical logical network.

A map information update method includes: (a) obtaining map information; (b) obtaining landmark observed positions indicating positions of one or more landmarks in a captured image; (c) adding that includes (i) generating added map information by adding information pertaining to the landmark observed positions to the map information, and (ii) updating the map information obtained in (a) to the added map information; (d) predicting that includes (i) calculating predicted map information based on the map information updated in (c), by using a neural network inference engine that has been trained, and (ii) updating the map information to the predicted map information; and updating information that includes (i) calculating updated map information based on the map information updated in (d), by using a gradient method, and (ii) updating the map information to the updated map information.

A method for predicting an ego-lane for a vehicle. The method includes: receiving at least one image captured by at last one camera sensor of the vehicle, which depicts a lane that may be used by a vehicle; ascertaining a center line of the lane, which extends through a center of the lane, by implementing a trained neural network on the captured image, the neural network being trained via regression to ascertain a center line of a lane, which extends in a center of the lane, based on captured images of the lane; outputting a plurality of parameters, which describe the center line of the lane, via the neural network; generating the center line based on the parameters of the center line; identifying the center line of the lane as the ego-lane of the vehicle; and providing the ego-lane.

A method is provided for creating at least one map of vehicle surroundings with the aid of a control unit. It is checked based on a comparison between received measured data and stored or received map data, whether first features, for example semantic features, are present and complete. First features available in a vehicle surroundings are extracted from the received measured data if no or incomplete map data are present. It is checked whether a localization is possible within the vehicle surroundings with the aid of the first semantic features. If a localization is imprecise or not possible with the aid of the ascertained first features, second features are extracted from the received measured data. A digital map of vehicle surroundings is created based on the ascertained first features and/or the second features. Furthermore, a control unit, a computer program as well as a machine-readable memory medium are provided.

A system for associating perceived and mapped lane edges can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived object; receive map data representing a map object; determine a cost matrix a cost matrix indicative of an association cost for associating the map object to the perceived object; compare the association cost with an association cost threshold; and associate the perceived object with the map object based on the association cost.

A system for generating linear feature data is provided. The system may determine, from sensor data, detection data associated with at least one link. The at least one link comprises a plurality of sub links. The system may further determine, using map data, one or more linear feature clusters for each of the plurality of sub links, based on the detection data. Furthermore, the system may determine a plurality of linear feature groups for the at least one link, based on at least one set of feature matched distances and the determined linear feature clusters, where a given linear feature group respectively comprises at least one first linear feature cluster associated with a first sub link and at least one second linear feature cluster associated with a second sub link. Furthermore, the system may generate the linear feature data, based on the plurality of linear feature groups.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.