Abstract: System and methods are provided for that allow users of micro mobility and shared mobility services to reduce the uncertainty related to the availability of a shared vehicle by quickly identifying such vehicles using a route that maximizes the visibility of the shared vehicle through line-of-sight computations.

Abstract: Disclosed is an approach for detecting LIDAR blind spots in parking garages comprising receiving, by one or more processors, floor layout data for at least a first floor of a parking garage, based on the received floor layout data for at least a first floor of a parking garage, detecting, by the one or more processors, at least one additional floor layout data for a floor of the parking garage, wherein the at least one additional floor has a substantially similar layout to the first floor of the parking garage, in response to detecting the at least one additional floor layout data for a floor of the parking garage, group by the one or more processors, the floors with substantially similar floor layout data into at least one cluster, making a determination, by the one or more processors, that at least one of the floor layout data in the cluster contains at least one LIDAR blind spot, and based at least on the determination, generating, by the one or more processors, LIDAR data for the at least one blind spot.

Abstract: Systems and methods for optimizing the notification of a modification to a route are provided. For example, a method of optimizing the notification of a modification to a route includes analyzing mobility data of an individual at a first location. The method also includes receiving at least one modification to a level of autonomous driving corresponding to one or more road segments between the first location and a second location. The method also includes, based on the at least one modification to the level of autonomous driving corresponding to the one or more road segments, determining one or more changes to at least one route between the first location and the second location. The method also includes providing a notification to the individual that includes information about the one or more changes to the at least one route between the first location and the second location.

Abstract: Systems and methods for selecting an autonomous vehicle software application are provided. For example, a method for selecting an autonomous vehicle software application includes receiving information corresponding to a vehicle trip in a given vehicle. The method also includes determining one or more elements corresponding to at least one of the vehicle trip and the given vehicle. The method also includes based on the determined one or more elements, selecting an autonomous vehicle software application for operation of the given vehicle.

Abstract: An approach is provided for computing an estimated time of arrival via a route based on a degraded state of a vehicle after an accident and/or malfunction. The approach involves receiving, by one or more processors, data indicating an operational status of a vehicle. The approach also involves processing, by the one or more processors, the data to determine a degraded operational state of the vehicle. The approach further involves computing, by the one or more processors, an estimated time of arrival, a route, or a combination thereof of the vehicle based on the degraded operational state.

Abstract: Systems and methods for determining a public transportation score are provided. For example, a method of determining a public transportation score includes receiving a public transportation metric associated with a first location within a public transportation network. The method also includes determining one or more elements of the public transportation network capable of affecting the public transportation metric. The method also includes determining a status of the one or more elements of the public transportation network. The method also includes based on the status of the one or more elements, determining a score corresponding to the public transportation metric.

Abstract: An apparatus, method and computer program product provide a route using a map layer of one or more sound events. For example, the apparatus receives a user preference indicating a sound type, identifies a road segment associated with the sound type from a plurality of road segments, selects a subset from the plurality of road segments as a route based on association of the subset with respect to the road segment, and outputs the route.

Abstract: A method, apparatus, and user interface for left turn indicator detection comprising obtaining image data of at least one navigable link, determining a left turn indicator based on the obtained image data, wherein the left turn indicator includes data from at least one driver profile, and associating the determined left turn indicator with one or more navigable links to update a map layer of a geographic database.

Abstract: Systems and methods for determining a route for mood improvement are provided. For example, a method of determining a route for mood improvement includes determining a mood of an individual. The method also includes receiving a destination as an input. The method also includes determining a route from a current location of the individual to the destination via a plurality of road segments for improving the mood of the individual.

Abstract: A method, apparatus and computer program product are provided for predicting a state of visibility for a road object. For example, at least one processor receives road sign attribute data indicating at least one attribute of a road sign. The processor further receives weather forecast data indicating a weather forecast of a location in which the road sign is disposed, and using the road sign attribute data and the weather forecast data, a state of visibility for the road sign is identified.

Abstract: A method, apparatus, and user interface for determining appointment attendance probability comprising obtaining an indication of least a first location and second location of an end user; wherein the first location and second location are obtained at a predefined time interval, obtaining end user driving profile data, obtaining end user appointment data, determining appointment attendance probability based, at least in part, on the obtained first location, second location, end user appointment data, and end user driving profile data, and updating at least one database with the determined appointment attendance probability.

Abstract: A method, apparatus, and user interface for road debris detection comprising, in some embodiments, obtaining image data of at least one navigable link and/or road debris, determining a road debris indicator based on the obtained image data, wherein the road debris indicator includes road debris classification data, and associating the determined road debris indicator with one or more navigable links to update a map layer of a geographic database.

Abstract: An apparatus, method and computer program product are provided for predicting improper parking events within electric vehicle charging locations. In one example, the apparatus receives input data indicating whether a first charging station is being used and a first pattern of frequency in which the first charging station is used. The apparatus generates an output data indicating a likelihood in which a first vehicle is occupying a first parking space designated for the first charging station and is electrically disconnected from the first charging station. The apparatus generates the output data as a function of the input data by using historical data indicating events in which second vehicles occupied second parking spaces designated for second charging stations and were electrically disconnected from the second charging stations. The historical data indicate a second pattern of frequency in which each of the second charging stations is used.

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 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: A network apparatus, for example, obtains probe data corresponding to a respective vehicle making a late lane change while traversing a TME of a traversable network. Location information indicating a location of the late lane change is extracted from the probe data. Map, weather, and/or traffic data for the location is obtained. A late lane change feature description is generated based on information extracted from the probe data and the map, weather, and/or traffic data. A model is trained using a machine learning technique and training data comprising the late lane change feature description. The model is executed to generate a late lane change prediction corresponding to a TME of a digital map. The network apparatus causes at least one of (a) the digital map to be updated, (b) traffic data corresponding to the TME to be updated, or (c) a navigation-related function to be performed based on the prediction.

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: 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: An approach is provided for machine learning of vehicular wait events. The approach, for instance, involves processing a sensor observation to determine a wait event. The wait event indicates that at least one person is in a wait state. The approach also involves processing the sensor observation to determine one or more contextual features associated with a location of the wait event, a time of the wait event, the at least one person, or a combination thereof. The approach further involves determining a ground truth of the wait state. The approach further involves vectorizing the one or more contextual features and the ground truth into a training vector. The approach further involves using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors. The approach further involves providing the trained machine learning model as an output.

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.