Abstract: Embodiments described herein may provide a method for identifying the location of a hazard, evaluating how long that hazard is likely to be present at the location, and providing an indication of the time to live of the hazard. Methods may include: receiving an indication of a hazard event, where the hazard event includes a timestamp and a location; map matching the location of the hazard event to a map-matched road segment; determining a time to live of the hazard event based, at least in part, on the timestamp and the map-matched road segment; providing an indication of the time to live of the hazard event as at least one of a database entry or a map user interface element; and providing for at least one of route guidance or navigational assistance based, at least in part, on the time to live of the hazard event.

Abstract: An apparatus, method and computer program product are provided for predicting events in which vehicles damage roadside objects. In one example, the apparatus receives input data indicating a first roadside object and contextual information associated with the first roadside object. The apparatus causes a machine learning model to generate output data as a function of the input data, wherein the output data indicate a likelihood in which one or more first vehicles will damage the first roadside object. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which second vehicles damaged second roadside objects. The historical data indicate first attributes associated with surroundings of the second roadside objects, second attributes associated with the second vehicles, and route maneuver information associated with the second vehicles.

Abstract: An approach is provided for resolving an inconsistency in road closure data stored in a mapping platform. The approach, for example, involves processing map data to generate a roadway graph representing a spatial relationship between a first road segment and a second road segment. The spatial relationship indicates that a first closure state of the first road segment cannot differ from a second closure state of the second road segment. The approach also involves determining that the inconsistency in the road closure data for the first road segment and the second road segment indicates that first closures state and the second closure state do not match. The approach further involves changing the road closure data stored in the mapping platform either to match the first road closure state with the second road closure state, or to match the second road closure state with the first road closure state in response to the inconsistency.

Abstract: An apparatus and method are disclosed for obtaining a simulated preview that depicts a representation of one or more user interface changes associated with an application plug-in. The approach involves, for example, rendering the simulated preview overlaid on a user interface of an application executing on a device. The rendering aligns the representation of the one or more user interface changes over the user interface to depict a preview of the one or more user interface changes that will occur after an installation of the application plug-in. The approach also involves rendering the simulated preview along with one or more simulated previews of installed plug-ins overlaid in a staked view on the user interface of the application. Addition and/or deletion of plug-ins can be managed in the stacked view.

Abstract: An approach is disclosed for detecting cornering spots capable of facilitating crowd control by limiting a mobility of a crowd. The approach involves, for example, collecting satellite, aerial, and/or street-level imagery associated with one or more geographic features of a geographic area. The approach also involves determining at least one cornering spot along with one or more attributes of the cornering spot characterizing the effectiveness of the cornering spot to assist in crowd control. Cornering spot data can be stored in a geographic database and/or displayed in an application on user equipment.

Abstract: An apparatus for sign detection includes a point cloud analysis module, an image analysis module, a frustum comparison module, and a sign detector. The point cloud analysis module is configured to receive point cloud data associated with a geographic region and classify at least one point neighborhood in the point cloud data as planar and a sign position candidate. The image analysis module is configured to receive image data associated with the geographic region and calculate a sighting frustum from the image data. The frustum comparison module is configured to perform a comparison of the sighting frustum to the sign position candidate having at least one point neighborhood classified as planar. The sign detector is configured to provide a location for the sign detection in response to the comparison of the sighting frustum to the sign position candidate.

Abstract: An apparatus, method and computer program product are provided for predicting events in which drivers fail to see curbs while the drivers are maneuvering vehicles. In one example, the apparatus receives vehicle attribute data associated with a first vehicle, map data indicating one or more attributes of a road portion including a first curb, and sensor data indicating an orientation of a first driver within the first vehicle. The apparatus causes a machine learning model to render an output as a function of the vehicle attribute data, the map data, and the sensor data. The output indicates a likelihood of which the first driver will not be able to see the first curb at the road portion when the first driver is maneuvering the first vehicle. The machine learning model is trained to predict the output based on historical data indicating events in which second drivers maneuvered second vehicles to encounter the first curb or one or more second curbs.

Abstract: Methods, systems, apparatuses, and computer program products are provided that are configured to processor sensor data using a single component specialized sensor data processing neural network. The single component data processing system is configured to receive input data from a sensor, analyze the input data using a neural network embodied by a single trained sensor data processing component, wherein the single trained sensor data processing component is trained to product output data that approximates a plurality of task-specific transformations performed in a pipeline manner, and produce the output data following transformation of the input data. Additionally, methods, systems, apparatuses, and computer program products are provided for training a single trained sensor data processing component to approximate a plurality of task-specific transformations performed in a pipeline manner.

Abstract: Methods, apparatus and computer program for identifying canopy structures are provided. In one aspect, a method includes receiving a first calculated offset between a start point of a shadow cast by a building and a proximate first vertex of a building footprint of the building, and receiving a second calculated offset between an end point of the shadow cast by the building and a proximate second vertex of the building footprint, the shadow identified from an overhead image of the building, and wherein the building footprint comprises a shadowed side and a non-shadowed side, each running between the start point and the end point of the shadow on a different respective side of the building. The method also includes comparing the offsets to an offset threshold, and in response to both the first and second received offsets exceeding the offset threshold, determining that the building footprint represents a canopy.

Abstract: A network apparatus determines a route from an origin traversable map element (TME) to a target TME based on map data of a network version of a digital map. The route includes a list of route TMEs to be traveled from the origin TME to the target TME. The network apparatus accesses map version agnostic information identifying each TME of the list of route TMEs from the network version of the digital map; generates a map version agnostic identifier for each route TME of the list of route TMEs based on the accessed information; evaluates coding functions based at least on the map version agnostic identifier for each route TME to generate a coded identifier for each route TME; generates an encoding data structure based on the coded identifiers for the route TMEs; and provides the encoding data structure.

Abstract: An approach is provided for detecting lane departure events based on map data and probe data. The approach, for example, involves map-matching probe data to a lane of a road segment. The probe data is collected from one or more sensors of at least one vehicle and/or at least one user device that traversed the road segment. The approach also involves processing the probe data to detect at least one lane departure event. The approach further involves categorizing the at least one lane departure event as an intentional lane departure event or an unintentional lane departure event. The approach further involves creating a lane departure warning message for the road segment based on the at least one categorized lane departure event, and/or road segments associated with multiple lane departure warning messages within a certain time period. The approach further involves providing the lane departure warning message as an output.

Abstract: An apparatus, method and computer program product are provided for predicting events in which drivers render aggressive behaviors while maneuvering vehicles. In one example, the apparatus receives input data indicating a target location and including attribute data associated with the target location. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which a target driver will render an aggressive behavior at the target location while maneuvering a target vehicle. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which drivers rendered the aggressive behavior while maneuvering vehicles.

Abstract: A system, method, apparatus, computer readable medium, and user interface for generating a navigation route based on weather manipulation data comprising: receiving weather manipulation data for a geographic or a three-dimensional region associated with at least one candidate route, wherein the weather manipulation data indicates whether there is at least one manmade environmental factor present; and generating the navigation route based on the weather manipulation data for at least one candidate route.

Abstract: An approach is provided for detecting an on-boarding or off-boarding event based on mobile device sensor data. The approach, for example, involves retrieving sensor data collected from at least one sensor of a mobile device that is fixed in a stationary position relative to a vehicle. The approach also involves processing the sensor data to determine roll angle data for the vehicle over a time window. The approach further involves processing the roll angle data to determine one or more transitions of a roll angle value of the vehicle between one or more value levels. The approach further involves determining an on-boarding event, an off-boarding event, or a combination thereof based on the one or more transitions. The approach further involves providing the on-boarding event, the off-boarding event, or a combination thereof as an output.

Abstract: An apparatus is configured to perform a method for generating warning polygons for weather events in a geographic region. The method includes receiving measurement data from one or more sensors, identifying at least one location from the measurement data, identifying a map tile within a predetermined distance to the at least one location, defining an index including the map tile in response to identification that the map tile is within the predetermined distance to the at least one location, calculating a confidence level for the map tile accessed from the index, comparing the confidence level for the map tile to a threshold confidence level, clustering the map tile in a map tile cluster in response to the confidence level exceeding the threshold level, and calculating a polygon based on the map tile cluster such that the polygon intersects the geographic region.

Abstract: An apparatus, method and computer program product are provided for predicting overtaking maneuver events. In one example, the apparatus receives input data including attribute data associated with a location and first travel data associated with a first occupant of a first vehicle. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which the first vehicle will execute an overtaking maneuver at the location. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which second vehicles executed the overtaking maneuver. The historical data include second travel data associated with second occupants of the second vehicles.

Abstract: A method, apparatus, and user interface for providing a carjacking detection and prediction system is disclosed. For example, image data of at least one vehicle may be obtained and a carjacking indicator for the vehicle based on the obtained image data is determined. One or more road segments may then be identified, and the determined indicator is associated with the one or more identified road segments to update a map layer of a geographic database and/or perform other actions.

Abstract: A method, apparatus and computer program product facilitate corrections to be made to a location determined utilizing a satellite-based positioning technique to reduce the errors introduced by atmospheric delay and/or advance of navigation signals propagating through an atmospheric layer, such as the ionosphere. In the context of a method for enabling a customized request for corrections to be made, the method includes determining a perimeter of a grid within which the corrections are sought and transmitting, toward a service provider, a request that identifies one or more correction points within the perimeter of the grid. In response to the request, the method includes receiving information associated with the corrections. The method also includes determining a position based upon the satellite-based positioning technique in combination with the received information associated with the corrections.

Abstract: A method, apparatus, and user interface for a lingering communication detection system comprising obtaining data of at least one end user electronic communication and determining a lingering communication indicator based on the obtained data, identifying one or more road segments, and associating the determined indicator with one or more identified road segments to update a map layer of a geographic database.

Abstract: An apparatus, method and computer program product are provided for predicting slipping events for micromobility vehicles. In one example, the apparatus receives input data indicating a target location and including contextual data associated with the target location. The apparatus causes a machine learning model to generate output data as a function of the input data. The output data indicate a likelihood in which a target micromobility vehicle will slip at the target location. The machine learning model is trained to generate the output data as a function of the input data by using historical data indicating events in which micromobility vehicles have slipped. The historical data indicate slip-inducing objects within locations of the events, proximity of sources of the slip-inducing objects relative to the locations, and one or more factors that cause the slip-inducing objects to be disposed within the locations.