Abstract: An approach is provided for operating a vehicle using vulnerable road user data. The approach, for example, involves determining a road link on which the vehicle is traveling or expects to travel. The approach also involves querying a geographic database for a vulnerable road user attribute of the road link. The approach further involves providing a notification to activate or deactivate an autonomous driving mode of the vehicle, a vehicle sensor of the vehicle, or a combination thereof while the vehicle travels on the road link based on determining that the vulnerable road user attribute meets a threshold criterion.

Abstract: An approach is provided for estimating a vulnerable road user count in real time or near real time. The approach, for example, involves collecting one or more vulnerable road user observations from at least one vehicle traveling a road link. The approach also involves querying a geographic database for historical vulnerable road user data for the road link. The approach further involves determining a first variance for the one or more vulnerable road user observations and a second variance for the historical vulnerable road user data. The approach further involves calculating the vulnerable road user count by fusing the one or more vulnerable road user observations with the historical vulnerable road user data based on the first variance and the second variance.

Abstract: An approach is provided for determining a navigation route based on vulnerable road user data. The approach, for example, involves generating one or more candidate navigation routes for a vehicle. The approach also involves querying a geographic database for vulnerable road user data for a respective set of road links comprising each of the one or more candidate navigation routes. The approach further involves selecting the navigation route from among the one or more candidate navigation routes based on the vulnerable road user data.

Abstract: A method, system, and computer program product is provided, for example, for assisting a user of the vehicle in following a prescribed speed limit while travelling in the vehicle. The method comprises providing a speed limit related recommendation to the user of the vehicle. The method further comprises receiving an image data representing a posted sign, in a vicinity of the vehicle, the image data being detected by one or more image capturing devices. Additionally, the method comprises processing the image data received to determine an observed numerical value. The method further comprises calculating, by a processor, a sign reliability factor based on a function of at least the observed numerical value, a historic mean of one or more vehicle speeds at the time of sign observation and a standard deviation of the one or more vehicle speeds at the time of sign observation. Additionally, the method may include providing data for an indication of the speed limit to be followed by the vehicle.

Abstract: An approach is provided for mapping vulnerable road users. The approach, for example, involves receiving sensor data from at least one vehicle indicating a presence of at least one vulnerable road user. The sensor data indicates a detected location of the at least one vulnerable road user. The approach also involves map matching the detected location of the at least one vulnerable road user to at least one road node, link, and/or a segment thereof of a geographic database. The approach further involves generating a vulnerable road user attribute to indicate a probability of the presence of the at least one vulnerable road user based on the sensor data. The approach further involves storing the vulnerable road user attribute in the geographic database as an attribute of the at least one road node, link, and/or segment.

Abstract: A method, system, and computer program product is provided, for example, for filtering road signs. The method comprises receiving a road sign observation information from a vehicle on a link. The road sign information includes at least a location of the vehicle. The method further comprises retrieving a sign file information from a database. The sign file information includes location of a plurality of VSS gantries in a geographical region. The method further includes filtering the road sign observation information if the location of the vehicle is within a predetermined distance threshold from at least one VSS gantry from the plurality of VSS gantries in the geographical region.

Abstract: A method, system, and computer program product is provided, for example, for matching a road sign observation information on a map application. The method may include receiving the road sign observation information. The method may further include identifying a plurality of candidate links based on a first distance between a location of the road sign observation information detection and a plurality of shape points. Additionally, the method may include calculating a heading difference between each of the plurality of candidate links and the location of the road sign observation information detection. Furthermore, the method may include identifying one or more qualified links from the plurality of candidate links, wherein the heading difference between each of the one or more qualified links from the plurality of candidate links and the location of the road sign observation information detection is within a predetermined heading difference threshold.

Abstract: A method, system, and computer program product is provided, for example, for generating heading information. The method comprises receiving a coordinate information for a gantry. The method further comprises receiving an identification information for a link associated with the gantry. Additionally, the method comprises extracting information related to one or more shape points associated with the link based on the identification information. Further, the method comprises matching the coordinate information for the gantry with at least two consecutive shape points from the one or more shape points, wherein the at least two consecutive shape points are closest from the gantry. Also, the method comprises calculating, by a processor, a heading value for the gantry based on the matching. The heading value for the gantry may be used to identify direction information for the gantry, which may further be used for updates.

Abstract: An approach is provided for detecting a presence of a physical divider on a road segment. The approach, for example, involves receiving sensor data from a vehicle traveling a road segment. The sensor data indicates a distance from the vehicle to the physical divider, a cross-sensor consistency of detecting the physical divider between at least two sensors of the vehicle, or a combination thereof. The approach also involves determining that the sensor data indicates the presence of the physical divider based on determining that the distance is within distance criteria, the cross-sensor consistency is within consistency criteria, or a combination thereof. The approach further involves updating data provided by a physical divider signal from the vehicle to indicate the presence of the physical divider on the road segment.

Abstract: An approach is provided for detecting a presence of a physical divider on a road segment. The approach, for example, involves determining a number of positive observations of the physical divider on the road segment based on sensor data collected from one or more vehicles traveling the road segment. The approach also involves determining a number of negative observations of the physical divider on the road segment based on the sensor data. The approach further involves calculating a probability of the presence of the physical divider based on the number of the positive observations and the number of the negative observations. The approach further involves updating map data for the road segment to indicate the presence of the physical divider based on determining that the probability of the presence of the physical divider is greater than a threshold value.

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 approach is provided for generating a volatility index for weather data. The approach involves retrieving weather data collected from one or more weather sensors over a temporal domain, a spatial domain, or a combination thereof. The one or more weather sensors provide the weather data for at least one geographic point. The approach also involves processing the weather data to determine volatility data for at least one weather attribute, wherein the volatility data represents how much the at least one weather attribute changes over the temporal domain, the spatial domain, or a combination thereof. The approach further involves generating a volatility index to represent the volatility data.

Abstract: An approach is provided for propagating learned traffic sign data. The approach involves, for example, determining a road link to which learned traffic sign data has been assigned. The approach also involves identifying one or more downstream links connected to the road link to which no learned traffic sign data has been assigned. The approach further involves propagating the learned traffic sign data of the road link to the identified one or more downstream links.

Abstract: An approach is provided for traffic sign learning based near a ramp. The approach involves, for example, determining that a learned sign (e.g., a traffic speed speed) is located within a distance threshold of a junction between a main road link and a ramp road link. The approach also involves assigning a learned sign value (e.g., a speed value) of the learned sign to the main road link, the ramp road link, or a combination thereof based on a topology of the junction, a lateral offset of the learned sign, an upstream sign value, or a combination thereof. The topology of the junction indicates a side of the main road link that the ramp road link occurs. The lateral offset of the learned sign is with respect to the main road link, the ramp road link, or a combination thereof.

Abstract: An approach is provided for determining a negative observation of a road feature (e.g., traffic sign). The approach involves, for example, querying a spatial data structure for a set of location points based on a spatial radius around a location of the road feature. The spatial data structure stores a plurality of location points from a plurality of location traces that did not have a road feature observation. The approach also involves map matching each location trace in the set of location points to a matched path of road links. The approach further involves determining that said each location trace is a negative observation of the road feature based on determining that the location of the road feature falls between two map-matched location points of said each location trace that are on the matched path of road links.

Abstract: An approach is provided for traffic sign learning. The approach involves, for example, receiving a plurality of traffic sign observations generated using sensor data collected from a plurality of vehicles. Each of the plurality of traffic sign observations includes location data and sign property data for an observed traffic sign corresponding to said each of the plurality of traffic sign observations. The approach also involves clustering the plurality of traffic speed sign observations into at least one cluster based on the location data and the sign property data. The approach further involves determining a learned sign for the at least one cluster, and determining a learned sign value indicated by the learned sign based on the location data, the sign property data, or a combination of the plurality of traffic sign observations aggregated in the at least one cluster.

Abstract: An approach is provided for traffic sign learning based on road network connectivity. The approach involves, for example, receiving data indicating a candidate traffic sign for a target road link. The approach also involves determining either (1) an upstream road attribute value indicated by an upstream traffic sign occurring in an upstream portion of the target road link or in an upstream road link with upstream connectivity to the target road link, or (2) an upstream mapped road attribute value of the upstream road link. The approach further involves calculating a difference between a road attribute value indicated by the candidate traffic sign and either the upstream road attribute value or the upstream mapped road value. The approach further involves assigning the candidate traffic sign and/or its candidate road attribute value to the target road link when the calculated difference is less than a threshold difference.

Abstract: A method, apparatus and computer program products are provided for matching a transit vehicle to a trip. An example method may comprise receiving probe data, the probe data comprising at least one of identity information, location information, or time information corresponding to the transit vehicle, causing placement of the probe data into a queue, wherein a placement in the queue is dependent on how much of the trip the transit vehicle has completed as indicated by the probe data, and causing assignment of the transit vehicle to a single candidate trip from among a set of candidate trips comprising a scheduled arrival time at a next closest stop closest to a calculated arrival time.

Abstract: A method, apparatus and computer program products are provided for grouping a plurality of vehicles together into a platoon and using navigation and other available data to optimize the efficiencies of operating the vehicles together as a platoon. Methods may include receiving a first trip request including a first vehicle identification, trip destination, and associated preferences; receiving a second trip request including a second vehicle identification, trip destination, and associated preferences; and generating a platooning plan including assignment of platoon leader to the first vehicle identification and a joining location where a vehicle of the first vehicle identification is to form a platoon with a vehicle of the second identification. Methods may include joining the first vehicle and the second vehicle in a secure vehicle-to-vehicle communication session.

Abstract: An approach is provided for machine learning of physical dividers. The approach, for instance, involves retrieving map data, sensor data, or a combination thereof for a segment of a road. The approach also involves retrieving ground truth data for the segment of the road. The ground truth data, for instance, indicates a true presence or a true absence of the physical divider on the segment of the road. The approach further involves processing the map data, the sensor, or a combination thereof and the ground truth data to train a machine learning model to predict the physical divider using the map data, the sensor data, or a combination thereof as an input. The approach further involves using the trained machine learning model to a generate a physical divider overlay of a map representation of a road network.