Abstract: Systems and methods for determining an attention level of an occupant of a vehicle are provided. For example, a method for determining an attention level of an occupant of a vehicle includes receiving sensor data associated with an occupant of a vehicle traveling along a road segment. The method also includes based on the sensor data, determining an attention level of the occupant corresponding to the road segment. The method also includes encoding the attention level in a database to facilitate one or more aspects of vehicle operation for vehicles travelling along the road segment.

Abstract: System and methods for determining traffic violation hotspots based on roadway feature and/or sensor data. Traffic violation hotspots include, for example areas where traffic violations are more likely to occur such as traffic light jumping, wrong way driving, over speeding, not wearing seatbelt, avoiding stop signs and red lights, distracted driving, passing other vehicles in a no-passing zone, among others. Traffic violation data from one region is used to train a model that is applied to a second region for which traffic violation data does not exist or is limited.

Abstract: An apparatus, method and computer program product are provided for providing cloud-based verification of edge-based detection. In one example, the apparatus generates a result of sensor data, wherein the sensor data is acquired by a sensor of an edge-based device, and wherein the result is processed by the edge-based device. The result indicates: (i) a confidence at which the sensor data is classified as a category; (ii) a degree at which the sensor data differ from a data point of map data, the data point associated with a location in which the sensor data was acquired; or (iii) a combination thereof. The apparatus causes a cloud-based device to process the sensor data in response to: (i) the confidence indicating uncertainty; (ii) the degree exceeding a threshold; or (iii) a combination thereof.

Abstract: An apparatus, method and computer program product are provided for providing a map layer of light-based attributes. In one example, the apparatus receives sensor data associated with a road segment and generates a data point for the map layer based on the sensor data. The data point indicates one or more light-based attributes of the road segment. The apparatus stores the data point in a database associated with the map layer. The map layer comprises the data point and one or more other data points that indicate the light-based attributes of one or more other road segments.

Abstract: A method and system are disclosed for calculating a friction index for an indoor space to identify routing through the indoor space that avoids high friction regions, where friction is impedance to movement caused by obstructions. The system collects and processes data from sensors that detect attributes of obstructions and/or sensors that detect environmental conditions that indicate the presence of obstructions to calculate the friction index. Sensors can be location based or carried by persons. The system provides a navigation route that avoids high friction regions as an output.

Abstract: System and methods are provided for predicting, reacting to, and avoiding loss of traction events, such as hydroplaning for autonomous vehicles. For example, the method predicts a risk of an area being subject to hydroplaning using real-time data and/or historical data. The method may store possible hydroplaning events in a geographic map along with a risk assessment. The method provides lane level hydroplaning risk predictions and avoidance mechanisms.

Abstract: A method and apparatus for assignment of a shared vehicle is described. The method includes receiving user location data, receiving booked shared vehicle data for a booked shared vehicle, determining booked shared vehicle suitability data using the user location data and the booked shared vehicle data, receiving alternative shared vehicle data for an alternative shared vehicle, determining an alternative shared vehicle suitability data using the user location data and the alternative shared vehicle data, and providing a notification to a user based on a comparison of the alternative shared vehicle suitability data to the booked shared vehicle suitability data.

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: A method, apparatus and computer program product are provided for automatically identify wrong-way driven vehicles in real-time on a roadway. Methods may include: receiving an image from at least one image sensor captured at a location; processing the image using a computer vision model to detect features within the image; identifying, using the computer vision model, a wrong-way driven vehicle in the image; incrementing a confidence value for detection of the wrong-way driven vehicle; and generating an alert indicating the presence of the wrong-way driven vehicle at the location in response to the confidence value satisfying a predetermined value.

Abstract: Provided herein is a method for a framework to predict the population density for an area based on indirect measurements and contextually similar areas. Methods may include: receiving ground truth population data corresponding to a first region; determining map features associated with the first region; receiving dynamic mobility data associated with the first region; training a machine learning model based on the ground truth population data corresponding to the first region, the map features associated with the first region, and the dynamic mobility data associated with the first region; receiving dynamic mobility data associated with a second region; determining map features associated with the second region; processing the dynamic mobility data associated with the second region and the map features associated with the second region using the machine learning model; and receiving, from the machine learning model, a population estimate for the second region.

Abstract: Systems and methods for evaluating vehicle occupant behavior are provided. For example, a method of evaluating vehicle occupant behavior includes analyzing behavior of an occupant of a vehicle associated with a trip in the vehicle. The method also includes determining one or more aspects of the trip in the vehicle. The method also includes determining a score of the behavior of the occupant of the vehicle based on the analysis and the one or more aspects.

Abstract: An approach is provided for confirming road vector geometry based on aerial image(s). For example, the approach involves retrieving a feature and a vector representation of a road link. The approach also involves processing one or more aerial images depicting the road link to extract a list of spectral pixel values corresponding to the vector representation. The approach further involves determining a degree of misalignment between the spectral pixel values and a spectral signature of the feature of the road link. The approach further involves initiating a confirmation of a geometry of the vector representation based on the degree of misalignment. The approach further involves providing the confirmation as an output.

Abstract: Systems and methods for traffic control and route guidance are provided. In some aspects, a method for controlling traffic includes receiving vehicle information for a vehicle navigating a location on a road network and identifying a maneuver of the vehicle at the location based on the vehicle information. The method also includes determining an intrusiveness of the maneuver made by the vehicle, and controlling traffic at the location based on the intrusiveness of the vehicle.

Abstract: An apparatus, method and computer program product are provided for determining a state of visibility of a road object, such as a road sign, using vehicle sensor data. For example, the apparatus determines whether one or more sensors of a first vehicle observes a road sign. If the road sign is not observed by the one or more sensors, the apparatus determines whether one or more second vehicles is obscuring the road sign. If the one or more second vehicles is not obscuring the road sign, the apparatus determines whether the road sign is obscured due to a weather condition. If the road sign is obscured due to the weather condition, the apparatus generates a signal indicating that the road sign was obscured due to the weather condition.

Abstract: Systems and methods for evaluating a review of a passenger trip are provided. For example, a method of evaluating a review of a passenger trip includes analyzing a review of a passenger trip in a vehicle. The method also includes receiving sensor data that corresponds to the passenger trip. The method also includes determining a score of the review based on the received sensor data and the analysis of the review.

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: An approach is provided for providing mobility insight data related to shared vehicles for a point of interest (POI). The approach involves retrieving shared vehicle data for the POI. The shared vehicle data indicates one or more shared vehicle events that have occurred, that are occurring at a given time, or a combination thereof within a threshold proximity of the POI. The approach also involves processing the shared vehicle data to determine mobility insight data. The mobility insight data includes a shared vehicle usage pattern, a shared vehicle availability pattern, or a combination thereof under one or more contexts for travel to or from the POI. The approach further involves presenting the mobility insight data in a location-based user interface.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

Abstract: An approach is provided for identifying partitions associated with erratic pedestrian behaviors and their correlations to points of interest. For example, the approach involves receiving sensor data associated with a geographic area. The approach also involves based on the sensor data, determining pedestrian-behavior parameter(s) respectively for partition(s). Each respective partition of the partition(s) represents a respective subarea of the geographic area, a respective time period, or a combination thereof. The approach further involves identifying at least one erratic partition from the partition(s) based on determining that a respective pedestrian-behavior parameter associated with the at least one erratic partition deviates from a baseline pedestrian-behavior parameter by at least a threshold extent. The approach further involves determining a correlation of the at least one erratic partition to at least one map feature of a geographic database.