Abstract: An approach is provided for managing pseudonymous or anonymous user data and relevant data management requests. The approach involves, for example, converting a numerical feature of a data point into a categorical form. The categorical form represents a value range into which a numerical value of the numerical feature falls. The approach also involves determining an identifier of a data contributor associated with the data point. The approach further involves concatenating the identifier with the categorical form. The approach further involves cryptographically hashing the identifier concatenated with the categorical form to generate a mark. The approach further involves associating the mark with the data point to generate marked pseudonymous-anonymous data. The approach further involves transmitting the pseudonymous-anonymous data to a data platform.

Abstract: System and methods for systems for special vehicle detection and notifications. Connected vehicle probe and sensor data is acquired and processed to detect the presence of a special vehicle using one or more machine learning models. Attributes are determined for the special vehicle based on additional information. An informational message/notification is composed and sent to vehicles in the vicinity of the special vehicle. Warnings may be provided for how to maneuver vehicles around the special vehicle.

Abstract: An apparatus configured to collect and predict charging data for an electric vehicle (EV) from a mobile device associated with a user or the EV or an infotainment unit of the EV.

Abstract: An approach is provided for an electric vehicle navigation system. For example, a destination is received as input. A geographic database is accessed to obtain data that represents a first road segment of a route to the destination. The data of the first road segment includes a first elevation coefficient of energy consumption associated with a first elevation change. The geographic database is accessed to obtain data that represents a second road segment of the route. The data of the second road segment includes a second elevation coefficient of energy consumption associated with a second elevation change. The first elevation coefficient is different than the second elevation coefficient. Energy consumption by the vehicle along the route is calculated based on the first elevation coefficient and the second elevation coefficient. Data representing energy consumption for the route is outputted as a function of the calculated energy consumption.

Abstract: An approach is provided for determining a confidence level for map feature identification in a map application. The approach involves, for instance, presenting an image of a map feature in a user interface of a device. The image is initially presented with a content of the image obscured from view. The approach also involves progressively un-obscuring the image in the user interface until an input identifying the map feature is received from a user via the user interface. The approach further involves determining a percentage of the image that is visible at a time the input is received from the user. The approach further involves personalizing an application to the user based on the feature identification confidence level.

Abstract: An approach is provided for traffic estimation/detection based on anomaly detection. The approach involves, for instance, retrieving probe data or other sensor data collected from sensors of devices traveling in a geographic area. The approach also involves aggregating the probe or sensor data into a sequence of frames. Each frame comprises a plurality of spatial cells representing the geographic area at a respective time interval. The approach further involves computing a similarity of the sequence to one or more historical sequences comprising historical frames of spatially and temporally binned historical probe data. The approach further involves determining a classification of a traffic state associated with the probe or sensor data as either a normal traffic state or as a traffic anomaly based on the similarity. By way of example, the traffic state of the probe data can then be estimated/predicted based on the classification.

Abstract: A method, apparatus and computer program product are provided for quantifying public transport coverage. In this regard, for a traveler journey beginning at a starting location, transit time data is determined. The transit time data is indicative of a predicted amount of time to travel between the starting location and a departure location associated with a public transport boarding location for a public transport. Furthermore, waiting time data is determined. The waiting time is indicative of a predicted amount of time to wait at the departure location prior to departure via the public transport. Additionally, time to departure data is computed based on a combination of the transit time data and the waiting time data. For the traveler journey, a time to departure probability prediction is also determined.

Abstract: An approach is provided for computer-vision-based object motion detection. The approach involves receiving a video sequence captured from a perspective of a vehicle/device traveling at street level. The approach also involves processing frames of the videos sequence using computer vision to determine semantic localization feature(s) associated with object(s) detected in the frames. The approach further involves clustering the object(s) into object cluster(s) across the frames based on the semantic localization feature(s). The approach further involves creating an object vector connecting the object(s) in a given cluster of the object cluster(s) across the frames. The approach further involves computing an angle between the object vector and a reference vector associated with a movement of the vehicle/device across the frames. The approach further involves classifying a motion of the object(s) relative to the vehicle/device based on the angle.

Abstract: An approach is provided for presenting a feedforward cue in a user interface before an upcoming vehicle event occurs. The approach involves retrieving map data covering an upcoming area in which a vehicle is to travel. The approach also involves extracting a feedforward cue from the map data, wherein the feedforward cue preemptively indicates an upcoming vehicle event that can have an effect on an experience that a user has while engaged in a non-driving activity in the vehicle. The approach further involves providing data for presenting the feedforward cue in a user interface before the upcoming vehicle event occurs.

Abstract: A method is provided to automatically create road and lane geometry from images representing map geometry within a geographical area using object detection. Methods may include: receiving a rasterized image representative of map geometry within a geographic area, where each pixel of the rasterized image includes a property representing at least one component of the map geometry; applying an object detection model to the rasterized image; generating a list of the bounding boxes together with classes of objects within the bounding boxes based on the object detection model; generating map data from the list of bounding boxes and the classes of objects within the bounding boxes; and updating a map in a map database with the map data.

Abstract: A vehicle mirror assembly is provided. The vehicle mirror assembly, for example, includes a housing attachable to a vehicle. The vehicle mirror assembly also includes a reflective surface coupled to the housing or covering an opening in the housing. The vehicle mirror assembly further includes a movable sensor coupled to the housing or contained in a space formed by the housing and reflective surface. The vehicle mirror assembly further includes an actuator assembly configured to modify an orientation, a position, or a combination thereof of the movable sensor in response to a control signal.

Abstract: An apparatus obtains a set of radio data comprising signal strength related values for radio signals transmitted by a transmitter with an association of each signal strength related value with a representation of a geographical location. The apparatus applies a frequency transform to the obtained set of radio data to obtain transform coefficients, each transform coefficient comprising a transform index and an associated transform value. The apparatus selects a subset of transform indices having more significant transform values than the remaining transform indices and compresses the transform indices by encoding each transform index exploiting a probability of occurrence of an index value of a respective transform index. The same or another apparatus decodes the compressed transform indices again for use in position operations.

Abstract: A method is provided for automatically creating road and lane geometry from images representing probe data within a geographical area using graph estimation. Methods may include: receiving a rasterized image representative of map geometry within a geographic area, where each pixel of the rasterized image includes a property representing at least one feature of the map geometry; identifying pixels within the rasterized image corresponding to nodes of one or more map elements as node pixels; determining, for a respective node pixel, presence of a next node based on the at least one property of the respective node pixel; determining for the respective node pixel, a location corresponding to the next node based on the at least one property of the respective node pixel; generating, from a sequence of node pixels, at least one map element; and updating a map in a map database with the at least one map element.

Abstract: A method, apparatus and computer program product are provided for surveillance of road environments via deep learning. In this regard, one or more features for vehicle sensor data associated with one or more vehicles traveling along a road segment proximate to a vehicle are determined. The vehicle includes one or more sensors that captures the vehicle sensor data. Furthermore, vehicle behavior data associated with the one or more vehicles is predicted using a machine learning model that receives the one or more features. The machine learning model is trained for detection of vehicle behavior based on historical vehicle sensor data and one or more rules associated with the road segment. The vehicle behavior data is also encoded in a database to facilitate modeling of vehicle behavior associated with the road segment.

Abstract: The disclosure provides a system, a method, and a computer program product for verification of traffic incident. The system may be configured to receive a traffic incident trigger. Based on the traffic incident trigger, the system may select one or more vehicles for verification of the traffic incident detected at a location. The system may determine a route of travel for each vehicle of the selected one or more vehicles based on the location of the detected traffic incident. The system may reroute the selected one or more vehicles on the respective route of travel determined for each vehicle. The selected one or more vehicles are rerouted to record data of the traffic incident. The system may further verify the detected traffic incident based on the data of the traffic incident recorded by the rerouted one or more vehicles.

Abstract: An apparatus, method and computer program product are provided for providing signature reduction for a vehicle. For example, the apparatus receives a destination for a vehicle as input, selects a subset from a plurality of road segments as a route from a location to the destination, and outputs the route or a portion thereof. The subset is selected based on association of each of the plurality of road segments with respect to a source, and the source is capable of acquiring vehicle signature information.

Abstract: A method is provided automatically creating map geometry from data from various sources gathered within a geographical area using data aggregation and conflation with statistical analysis. Methods may include: receiving observation data associated with a geographic area; rasterizing objects within the observation data onto corresponding channels in a raster image corresponding to the geographic area having a given resolution; determining a distribution of locations of the objects parameterized by values in different channels in the raster image from the observation data; extracting analytic geometries from the raster image; generating map geometry based on the extracted analytic geometries; and updating a map database with the generated map geometry.

Abstract: An approach is disclosed for computing a spatial footprint index. The approach involves, for example, receiving, by one or more processors, information indicating that one or more physical items are associated with a user, a set of users, or a combination thereof. The approach also involves determining, by the one or more processors, spatial data indicating a physical space occupied by the one or more physicals items. The approach further involves computing, by the one or more processors, a spatial footprint index for the user, the set of users, or a combination thereof based on the spatial data, wherein the spatial footprint index is a quantified representation of the physical space occupied by the one or more physical items. The approach further involves providing, by the one or more processors, the spatial footprint index as an output.

Abstract: An apparatus, method and computer program product are provided for predicting tire temperature levels. In one example, the apparatus receives input data indicating attributes of the target vehicle and attributes of target routes for the target vehicle. The apparatus causes a machine learning model to generate output data as a function of the input data, where the output data indicate a subset of the target routes of which the target vehicle can successfully traverse while using the at least one spare tire. The machine learning model is trained to generate the output data as a function of the input data based on training data, where the training data indicate events in which vehicles used spare tires to traverse routes. The training data indicate attributes of the vehicles and attributes of the routes.

Abstract: An approach is provided for location sharing using a LiDAR-based location signature. The approach involves, for example, receiving a Light Detection and Ranging (LiDAR) scan of a location captured by a LiDAR sensor of a portable device. The approach also involves processing the LiDAR scan to generate a LiDAR location signature that is representative of the location of the portable device. The approach further involves transmitting the LiDAR location signature to another device to share the location of the portable device with the another device.