Abstract:
An approach is provided for map data updates based on region-specific data turbulence. The approach involves, for example, retrieving historical map data for the map region and segmenting the historical map data into a time series including at least a first time epoch and a second time epoch. The approach also involves calculating a first representative value for the first time epoch based on the historical map data segmented into the first time epoch, and a second representative value for the second time epoch based on the historical map data segmented into the second time epoch. The approach further involves calculating the map data turbulence based on the first representative value and the second representative value.
Abstract:
An approach is provided for classifying one or more vehicles based on their level of automation. The approach involves determining training sensor data collected during at least one driving operation of one or more vehicles, wherein one or more automation levels of the one or more vehicles are known. The approach also involves determining one or more sensor signatures for the one or more automation levels based, at least in part, on one or more values of one or more classification features extracted from the training sensor data. The approach further involves causing, at least in part, a classification of one or more other vehicles according to the one or more automation levels based, at least in part, on the one or more sensor signatures and sensor data associated with the one or more other vehicles.
Abstract:
An approach for traffic incident verification involves receiving a traffic message channel (TMC) message indicating a traffic incident. The TMC message indicates an extent of the traffic incident in a road graph using TMC location code(s)/offset(s). The approach also involves matching the TMC location code(s)/offset(s) to a first set of map road link(s) and offset(s) of a geographic database, determining sensor data collected from vehicle(s) travelling within the road graph. The sensor data indicates the extent as location point(s) of topology segment(s) (TSs) and TS offset(s), matching the location point(s) of the TS(s) and TS offset(s) to a second set of map road link(s) and offset(s) of the geographic database, determining an intersection set between the first and second sets, and initiating a confirmation of the traffic incident reported in the TMC message based on the sensor data of the location point(s) of the TSs in the intersection set.
Abstract:
An apparatus, method and computer program product are provided for providing autonomous vehicle navigation at intersections. In one example, the apparatus identifies an intersection associated with at least two road signs having the same sign type and facing the same direction. The apparatus updates map data to include a datapoint that provides a representation of a single road sign at the intersection instead of the at least two road signs, thereby enabling an autonomous vehicle relying on the map data to traverse the intersection to adhere to the single road sign in lieu of the at least two road signs.
Abstract:
A system, a method and a computer program product are provided to predict vehicle parking bunching in a geographic region. For example, the system is configured to obtain a plurality of contextual features and/or a plurality of sensor data related to parking information in the geographic region. The system is configured to predict a vehicle parking bunching based on a vehicle unparking threshold, a vehicle separation distance cluster threshold and/or unparking vehicle information. The system may also be configured to alert a vehicle of the vehicle parking bunching with a vehicle parking bunching notification.
Abstract:
A system, a method, and a computer program product may be provided for generating navigation instructions. The system may include a memory configured to store computer executable instructions and a processor configured to execute the computer executable instructions to retrieve sensor data associated with at least one vehicle travelling along a route having at least one intersection and map data associated with the route from a map database. The sensor data indicates intersection information relating to the at least one intersection. The intersection information comprises an intersection category and an intersection type. The map data comprises traffic-related information for a plurality of intersection connected links. The processor may be further configured to determine a turn cost correction factor for the at least one intersection, based on the sensor data and the map data. The processor may be further configured to store the turn cost correction factor within the map database.
Abstract:
The disclosure provides a system, a method, and a computer program product for updating map data. The system, for example, obtains sensor data from one or more user equipment. The sensor data is associated with a road object. Further, the system, determines a first location of a road observation sight and a second location of the road object based on a timestamp associated with the first location. Further, a distance associated with the second location of the road object and a center point of a link, is calculated. The link is a map matched link associated with the second location. Further, the system updates the map data based on the calculated distance.
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 method, apparatus, and user interface are provided for generating personalized splines. For example, one or more geometries that represent a portion of a road network are identified and a first spline curve is calculated from the one or more geometries. One or more additional geometries that represent a different portion of the road network are then interpolated, by a processor, and the processor then calculates at least a second spline curve from the one or more additional geometries, wherein the second spline curve is derived, at least in part, from personalized driving preference data.
Abstract:
Embodiments described herein may provide a method for identifying hazard polygons in a geographic region from a plurality of sources and aggregating hazard polygons into a merged hazard polygon. Methods may include: receiving a first indication of a first hazard warning, where the first hazard warning includes a first hazard condition and a first hazard polygon in which the first hazard condition is estimated to be present; receiving a second indication of a second hazard warning, where the second hazard warning includes a second hazard condition and a second hazard polygon in which the second hazard condition is estimated to be present; generating, from the first hazard polygon and the second hazard polygon, a merged hazard polygon; and providing for at least one of navigational assistance or autonomous vehicle control based, at least in part, on the merged hazard polygon.