Abstract: The disclosure provides a method, a system, and a computer program product for predicting an accident. The method comprises capturing one or more audio signals based on one or more sensors onboard a vehicle. The method may include extracting one or more features associated with each of the one or more audio signals. The method further includes, generating an output for predicting the accident, based on the extracted one or more features associated with each of the one or more audio signals. Further, the output comprises a predicted accident state and an associated confidence value. Also, the predicted accident state comprising at least one of: a no accident state, a pre-accident state, a light accident state, and an intense accident state.

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: Embodiments described herein relate to anonymizing sensor data through the use of map data. Methods include: receiving sensor data defining a trajectory; map-matching the sensor data using a map-matching algorithm to a plurality of road segments of a map database to generate a sequence of map-matched sensor data elements; determining a first value representing anonymization associated with a start of the trajectory; determining a second value representing anonymization associated with an end of the trajectory; determining first map-matched sensor data elements at the start of the trajectory to be redacted based on the first value; determining second map-matched sensor data elements at the end of the trajectory to be redacted based on the second value; and transmitting sensor data associated with elements in the sequence of map-matched sensor data elements between the first map-matched sensor data elements and the second map-matched sensor data elements.

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: An apparatus, method and computer program product are provided for providing a the map including a binary representation of a weather-based hazard. In one example, the apparatus receives data indicating a polygon defining a region being impacted by a weather condition and overlays the polygon on a tile map. The apparatus receives attribute data indicating one or more functional classes of one or more road portions within each tile of the tile map that is partially covered by the polygon and determines a subset of tiles within the tile map that defines the weather condition based on the attribute data.

Abstract: An apparatus, method and computer program product provide a map layer of one or more temporary dynamic obstructions. For example, the apparatus is configured to receive vehicle/driver behavior data associated with a vehicle at a portion of a road, determine a likelihood of a temporary dynamic obstruction existing proximate to the portion based on the vehicle behavior data, and update a datapoint of a map layer based on the likelihood. The datapoint indicates a state of existence of the temporary dynamic obstruction at the portion.

Abstract: Embodiments described herein provide a method for using one or more audio signals from one or more sensors to establish the presence and severity of precipitation at a particular location. Methods may include: receiving at least one first audio signal from a first audio sensor of a vehicle; extracting acoustical features including frequency and amplitude from the at least one first audio signal; receiving at least one second audio signal from a second audio sensor of the vehicle; extracting acoustical features including frequency and amplitude from the at least one second audio signal; processing the frequency and amplitude from the at least one first audio signal and the frequency and amplitude from the at least one second audio signal as inputs to an algorithm to generate an output from the algorithm; and determining, from the output of the algorithm, a precipitation condition and a confidence measure of the precipitation condition.

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: 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: 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: 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 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: 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: A system, a method, and a computer program product may be provided for processing event data. For example, event data relating to an occurrence of an event corresponding to a first tile is received, and a first count value associated with the received event data is determined. A threshold count value associated with the event within the first tile is obtained, and dynamic switching between a link-based processing and a tile-based processing is performed, based on a comparison between the first count value and the threshold count value, wherein on determining the first count value to be greater than the threshold count value, the tile-based processing is performed, and on determining the first count value to be lesser than the threshold count value, the link-based processing is performed.

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: An approach is provided for determining a lane marking confusion index based on lane confusion event detections. The approach, for example, involves detecting, by at least one processor, a number of instances of a lane confusion event associated with a location in a road network. The lane confusion event indicates presence of temporary lane markings in addition to standard lane markings at the location. The approach also involves determining, by the at least on processor, a lane marking confusion index for the location based on the detected number of instances of the lane confusion event in association with the location. The approach further involves storing, by the at least one processor, the lane marking confusion index as a map attribute of the location in a geographic database.