Abstract: Various aspects of a system, a method, and a computer program product for generation of roadwork extension data of a roadwork zone are disclosed herein. In accordance with an embodiment, the system includes a memory and a processor. The processor may be configured to obtain speed funnel data of one or more speed funnels. The processor may be configured to determine a plurality of candidate roadwork links, based on the speed funnel data. The processor may be configured to obtain vehicular trajectory data corresponding to the plurality of candidate roadwork links. The processor may be further configured to determine at least one qualified roadwork link from the plurality of candidate roadwork links, based on the vehicular trajectory data and speed threshold data of the plurality of candidate roadwork links. The processor may be further configured to generate the roadwork extension data based on the at least one qualified roadwork link.

Abstract: An approach is provided for detecting a slippery road condition based on a friction measurement. The approach, for example, involves receiving a traction loss of a vehicle traveling on the road link. The traction loss is detected using a first sensor. The approach also involves receiving a coefficient of friction between the vehicle and a road surface of the road link. The coefficient of friction is measured using a second sensor. The approach further involves fusing the traction loss with the coefficient of friction to detect the slippery road condition on the road link. The approach further involves providing the detected slippery road condition as an output.

Abstract: A method, a system, and a computer program product may be provided for updating map data in a map database to indicate presence of a road sign. The method may include obtaining first sensor data associated with a road sign. The first sensor data may comprise at least one of one or more first positive observations of a road sign or one or more first negative observations of the road sign. The method may further include obtaining second sensor data for the road sign, for example, based on the first sensor data. The second sensor data may comprise cumulative historic road sign observation data for time duration. The method may include determining a sign confidence ratio based on the first sensor data and the second sensor data. The method may include updating the map data based on the sign confidence ratio.

Abstract: A method, a system, and a computer program product may be provided for generating at least one route speed funnel for roadwork zone identification. The method may include generating a plurality of learned road signs from a plurality of road sign observations captured by a plurality of vehicles, determining a plurality of primary speed funnels, and generating at least one route speed funnel from the plurality of primary speed funnels based on a route validity condition. Each of the plurality of primary speed funnels comprises a different pair of learned road signs selected from the generated plurality of learned road signs. The method may further include determining a plurality of candidate speed funnels and temporally validating the plurality of candidate speed funnels to obtain the plurality of primary speed funnels.

Abstract: A method, a system, and a computer program product are provided for updating one or more attributes of a learned road sign (LRS). The system, for example, comprising at least one non-transitory memory configured to store computer program code instructions; and at least one processor is configured to execute the computer program code instructions to obtain sensor data associated with the LRS and determine a plurality of first downstream links associated with the LRS, based on the sensor data. Further, the at least one processor is configured to select a candidate downstream link from the plurality of first downstream links based on a heading difference between the LRS and each of the plurality of first downstream link and to update the one or more attributes of the LRS, based on the candidate downstream link.

Abstract: A system, a method, and a computer program product for generating classification data of a sign. The method comprises obtaining map data of the sign, obtaining a map matched link and at least one parallel link associated with the sign based on the map data, determining presence data of at least one blocking link between the map-matched link and the at least one parallel link, wherein the at least one blocking link satisfies one or more of a heading criterion and a distance criterion. The method further comprises generating the classification data of the sign based on the presence data of the at least one blocking link.

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: 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 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, generating a map tile cluster based on analysis of the at least one map tile, accessing an end-use constraint of the end-use device, the end-use constraint indicative of a constraint in processing on the end-use device, and calculating the warning polygon based on the map tile cluster and based on the end-use constraint such that the polygon intersects the geographic region.

Abstract: An approach is provided for vehicle map data update. The approach involves, for example, processing sensor data captured by a sensor of a vehicle to detect a machine-readable visual representation. The machine-readable visual representation encodes map data corresponding to a map tile of a geographic database. The approach also involves decoding the map data from the machine-readable visual representation. The approach further involves updating a local copy of the map tile stored in the vehicle based on the map data.

Abstract: A method and a system are disclosed for predicting that a road object is in a road zone or not. The method may include receiving at least one road object observation associated with the road object; extracting at least one feature associated with the road object or surroundings thereof based on the received at least one road object observation; and predicting, using a trained machine learning model, that the road object is in the road zone or not based on the extracted at least one feature, wherein the machine learning model is trained based on a training data set comprising a combination of at least one training feature and a ground truth label data, wherein the ground truth label data comprises at least one of a road zone data and a non-road zone data.

Abstract: An approach is provided for generating parking restriction data using a machine learning model. The approach involves determining a plurality of classification features associated with a set of labeled road links. Each of the labeled road links is labeled with a parking restriction label that indicates a parking restriction status of said each of the labeled road links. The approach also involves training the machine learning model to classify an unlabeled road link of the geographic database using the plurality of classification features. The approach further involves determining the plurality of classification features for the unlabeled road link. The approach further involves processing the plurality of classification features for the unlabeled road link using the trained machine learning model to associate an assigned parking restriction label to the unlabeled road link. The approach further involves storing the assigned parking restriction label as the parking restriction data.

Abstract: A method, apparatus and computer program product are provided to predict a map object based at least in part on audio data. With respect to predicting a map object, audio data created by a vehicle while driving over a road surface is obtained. The audio data includes one or more audio amplitude features, one or more audio frequency features or a combination of audio amplitude features and audio frequency features. The audio data including the one or more audio amplitude features is provided to a machine learning model and the map object that created the audio data while the vehicle interacted therewith is predicted utilizing the machine learning model. A method, apparatus and computer program product are also provided for training the machine learning model to predict the map object based at least in part on audio data.

Abstract: A method, apparatus and computer program product are provided to identify the class of vehicle driving over a road surface based upon audio data collected as the vehicle drives thereover. With respect to predicting a class of a vehicle, audio data is obtained that is created by the vehicle while driving over the road surface. The audio data includes one or more audio frequency features and/or one or more audio amplitude features. The audio data including the one or more audio frequency features and/or the one or more audio amplitude features is provided to a machine learning model and the class of the vehicle that created the audio data is predicted utilizing the machine learning model. A method, apparatus and computer program product are also provided for training the machine learning model to predict the class of the vehicle driving over the road surface.

Abstract: The disclosure provides a method, a system, and a computer program product for validating path data for a main link path or a second link path. The method comprises receiving at least one traffic object observation based on sensor data. The method also includes extracting a plurality of path locations associated with the at least one traffic object observation. The method also includes determining an association between the plurality of path locations based on a statistical model. The method further includes generating a trajectory for the plurality of path locations based on the association and validating the path data for the main link path or the second link path, based on the generated trajectory.

Abstract: A road furniture item or another type of road object is detected by at least one sensor. An associated geographic position associated with the road furniture item or road object is determined. After a predetermined time is reached, the geographic position is compared to a local database. A remote database is updated after the predetermined time is reached and according to the comparison with the local database. In some examples, the data indicative of the road furniture item or the road object is deleted based on a confidence level in the local database.

Abstract: A system, method and computer program product are provided for classifying at least one speed sign associated with a region. In an example embodiment, the method may include obtaining sensor data comprising speed limit data associated with the at least one speed sign. The method may further include obtaining map data associated with a segment of the region, wherein the map data comprises conditional speed limit data associated with a conditional speed limit sign linked with the segment. The method may further include comparing the speed limit data with the conditional speed limit data and classifying the at least one speed sign as one of a conditional speed sign or a non-conditional speed sign based on the comparison.

Abstract: Sensor data captured by one or more sensors coupled to a first entity are received by an apparatus. The sensor data comprises a location of the first entity or representation of surroundings of the first entity. Based at least in part on the sensor data, a second entity is identified. Based at least in part on trajectories along which the first and second entities are respectively traveling, it is expected that the second entity will enter a zone defined around the first entity. Based at least in part on the sensor data, an amount of time until the second entity will enter the zone is determined. The amount of time until the second entity will enter the zone is provided as (a) input to a navigation function and/or (b) to be displayed as a countdown via a display element and/or speaker element of the first entity.

Abstract: A method, apparatus and computer program product are provided for vehicle localization via frequency audio features. In this regard, a frequency audio signature of audio data received from one or more audio sensors of a vehicle is determined. Location data associated with the vehicle is also determined. Based on the location data for the vehicle, at least a portion of an audio feature map is selected. The audio feature map stores frequency audio signatures associated with road noise in relation to respective locations. Furthermore, the frequency audio signature of the audio data is compared with the frequency audio signatures of the audio feature map. Based on the comparison between the frequency audio signature of the audio data and the frequency audio signatures of the audio feature map, a location of the vehicle is refined to generate updated location data for the vehicle.

Abstract: A method, apparatus and computer program product are provided for vehicle localization via amplitude audio features. In this regard, an amplitude audio signature of audio data received from one or more audio sensors of a vehicle is determined. Location data associated with the vehicle is also determined. Based on the location data for the vehicle, at least a portion of an audio feature map is selected. The audio feature map stores amplitude audio signatures associated with road noise in relation to respective locations. Furthermore, the amplitude audio signature of the audio data is compared with the amplitude audio signatures of the audio feature map. Based on the comparison between the amplitude audio signature of the audio data and the amplitude audio signatures of the audio feature map, a location of the vehicle is refined to generate updated location data for the vehicle.