Abstract: An approach is provided for determining travel direction and/or location data based on sequential semantic events. The approach, for example, involves processing sensor data collected from at least one sensor of a mobile device to determine a set of observed time-sequenced semantic events. The semantic events are associated with traveling within a transportation system, and at least one semantic event of the set of observed time-sequenced semantic events is based on a turn angle value determined from the sensor data. The approach also involves initiating a comparison of the set of observed time-sequenced semantic events against a set of known time-sequenced semantic events and/or a known piece of information associated with the transportation system. The approach further involves determining a direction of travel and/or a location of the mobile device within the transportation system based on the comparison. The approach further involves providing the direction of travel and/or the location as an output.

Abstract: The systems and methods disclosed may improve existing optical coupler and sparse coding problem solving technology. Optical coupler technology may be improved by the provision of a versatile, efficient, and rapid optical coupler that may be programmed. Sparse coding optimization technology may be improved by the provision of methods for converting a sparse coding optimization problem into a quadratic unconstrained binary optimization for minimization by an annealer (such as a programmable optical coupler), a quantum computer, or similar apparatus. When combined, the programmable optical coupler may solve sparse coding optimization problems particularly quickly and efficiently.

Abstract: An approach is provided for in-parking navigation based on mobile device sensor data, the resulted semantic events, and an estimated parking occupancy level. The approach, for example, involves receiving parking information from in-parking navigation system(s) of vehicle(s) and/or mobile device(s) associated with the vehicle(s) traveling in a parking facility with no or a positioning satellite coverage below a threshold. The parking information indicates parking spot location(s) occupied by the vehicle(s). The in-parking navigation system(s) determines the at least one parking spot location based on tracking multi-modal trajectories of the mobile device(s). The multi-modal trajectories comprise vehicle trajectory segment(s) traveling within the parking facility and pedestrian trajectory segment(s) traveling to/from pedestrian entry or exit point(s) of the parking facility. The approach also involves determining an occupancy level of the parking facility based on the parking information.

Abstract: An approach is provided for detecting an on-boarding or off-boarding event based on mobile device sensor data. The approach, for example, involves retrieving sensor data collected from at least one sensor of a mobile device that is fixed in a stationary position relative to a vehicle. The approach also involves processing the sensor data to determine roll angle data for the vehicle over a time window. The approach further involves processing the roll angle data to determine one or more transitions of a roll angle value of the vehicle between one or more value levels. The approach further involves determining an on-boarding event, an off-boarding event, or a combination thereof based on the one or more transitions. The approach further involves providing the on-boarding event, the off-boarding event, or a combination thereof as an output.

Abstract: An approach is provided for detecting an on-boarding or off-boarding event based on mobile device sensor data. The approach, for example, involves retrieving sensor data collected from at least one sensor of a mobile device that is fixed in a stationary position relative to a vehicle. The approach also involves processing the sensor data to determine roll angle data for the vehicle over a time window. The approach further involves processing the roll angle data to determine one or more transitions of a roll angle value of the vehicle between one or more value levels. The approach further involves determining an on-boarding event, an off-boarding event, or a combination thereof based on the one or more transitions. The approach further involves providing the on-boarding event, the off-boarding event, or a combination thereof as an output.

Abstract: An approach is provided for in-parking navigation based on mobile device sensor data, the resulted semantic events, and an estimated parking occupancy level. The approach, for example, involves receiving parking information from in-parking navigation system(s) of vehicle(s) and/or mobile device(s) associated with the vehicle(s) traveling in a parking facility with no or a positioning satellite coverage below a threshold. The parking information indicates parking spot location(s) occupied by the vehicle(s). The in-parking navigation system(s) determines the at least one parking spot location based on tracking multi-modal trajectories of the mobile device(s). The multi-modal trajectories comprise vehicle trajectory segment(s) traveling within the parking facility and pedestrian trajectory segment(s) traveling to/from pedestrian entry or exit point(s) of the parking facility. The approach also involves determining an occupancy level of the parking facility based on the parking information.

Abstract: An approach is provided for calibrating vehicle motion data using a rotation matrix calculated based on mobile device sensor data, thereby determining vehicle events (e.g., forward acceleration, stoppages, etc.). The approach, for example, involves determining a road segment that meets one or more criteria for straightness, inclination, or a combination thereof. The approach also involves collecting sensor data from at least one sensor of a mobile device associated with a vehicle in motion on the road segment based on the determination. The sensor data indicates one or more acceleration vectors in a mobile device frame of reference. The approach further involves calibrating the one or more acceleration vectors from the mobile device frame of reference to a vehicle frame of reference based on the sensor data. The approach further involves providing the one or more calibrated acceleration vectors as an output.

Abstract: An approach is provided for determining a vehicle parking event and respective characteristics using sensor data. The approach, for example, involves receiving sensor data from at least one sensor associated with a mobile device in a vehicle. The approach also involves processing the sensor data to determine a sequence of semantic events. The semantic events respectively indicate a maneuver performed by the vehicle. The approach further involves processing the sensor data to determine a distance estimation over which at least one of the semantic events is performed. The approach further involves detecting a parking event of the vehicle, a characterization of the parking event, or a combination thereof based on the sequence of semantic events and the distance estimation. The approach further involves providing the parking event, the characterization of the parking event, or a combination thereof as an output.

Abstract: An approach is provided for mapping a parking facility using multi-modal trajectories collected using mobile device(s). The approach, for example, involves collecting sensor data from sensor(s) of mobile device(s). The sensor data represents the multi-modal trajectories comprising (1) vehicle trajectory segments during which the device(s) traveling into/out of a parking facility, and (2) pedestrian trajectory segments during which the device(s) traveling to/from pedestrian entry/exit point(s) of the parking facility. The approach also involves processing the sensor data to determine semantic event(s) associated with parking in the parking facility based on the multi-modal trajectories. The approach further involves determining parking spot location(s) and/or orientation(s) of the parking facility based on the semantic event(s). The approach further involves generating map data indicating the parking spot location(s) and/or orientation(s). The approach further involves providing the map data as an output.

Abstract: An approach is provided for detecting a vehicle door opening or closing event based on sensor data collected during a vehicle idle/stop state. The approach, for example, involves collecting sensor data from one or more sensors of a vehicle, a mobile device in the vehicle, or a combination thereof. The approach also involves determining a time period when the vehicle is in an idle state based on the sensor data. The approach further involves determining a vehicle door open event or a vehicle door close event based on pressure sensor data from at least one pressure sensor of the mobile device collected during the time period. The approach further involves providing the vehicle door open event or the vehicle door close event as an output.

Abstract: An approach is provided for determining vehicle maneuver events. The approach, for example, involves determining sensor data from a sensor of a device associated with a vehicle. The sensor data includes a maneuver parameter represented using a device frame of reference. The approach also involves transforming the maneuver parameter from the device frame of reference to an Earth frame of reference. The approach further involves automatically detecting a maneuver event of the vehicle based on the maneuver parameter as transformed to the Earth frame of reference. The approach further involves providing the detected maneuver event to a signaling unit associated with the vehicle.

Abstract: An approach is provided for determining vehicle maneuver events. The approach, for example, involves determining sensor data from a sensor of a device associated with a vehicle. The sensor data includes a maneuver parameter represented using a device frame of reference. The approach also involves transforming the maneuver parameter from the device frame of reference to an Earth frame of reference. The approach further involves automatically detecting a maneuver event of the vehicle based on the maneuver parameter as transformed to the Earth frame of reference. The approach further involves providing the detected maneuver event to a signaling unit associated with the vehicle.

Abstract: An approach is provided for determining travel direction and/or location data based on sequential semantic events. The approach, for example, involves processing sensor data collected from at least one sensor of a mobile device to determine a set of observed time-sequenced semantic events. The semantic events are associated with traveling within a transportation system, and at least one semantic event of the set of observed time-sequenced semantic events is based on a turn angle value determined from the sensor data. The approach also involves initiating a comparison of the set of observed time-sequenced semantic events against a set of known time-sequenced semantic events and/or a known piece of information associated with the transportation system. The approach further involves determining a direction of travel and/or a location of the mobile device within the transportation system based on the comparison. The approach further involves providing the direction of travel and/or the location as an output.

Abstract: An approach is provided for determining vehicle speed. The approach, for example, involves determining sensor data from a magnetometer, an accelerometer, or a combination thereof associated with a vehicle. The approach also involves processing the sensor data to determine a rotational frequency of at least one tire of the vehicle. The approach further involves calculating a speed, a tire/wheel diameter, a safety condition, and/or a maintenance condition of the vehicle based on the rotational frequency and providing the speed, the tire/wheel diameter, the safety condition, and/or the maintenance condition as an output.

Abstract: An approach is provided for performing ride-sharing functions based on joint motion using multiple sensor data. The approach, for example, involves retrieving a joint motion prediction indicating whether at least two devices are traveling in a same transportation vehicle. The joint motion prediction is computed based on sensor data collected from the at least two devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves initiating a ride-sharing function for respective users of the at least two devices based on the joint motion prediction.

Abstract: An approach is provided for detecting joint motion using multiple sensor data. The approach, for example, involves retrieving sensor data at least two devices. The sensor data, for instance, is collected using at least one sensor type from among a plurality of sensor types and wherein, and each sensor type of the plurality of sensor types is associated with a respective joint motion classifier. The approach also involves processing the sensor data using the respective joint motion classifier for said each sensor type of the least one sensor type to compute a respective sensor-type joint motion prediction. The approach further involves processing the respective sensor-type joint motion prediction for said each sensor type using a unified classifier to compute a unified joint motion prediction for the at least two devices. The approach further involves providing the unified joint motion prediction as an output.

Abstract: An approach is provided for detecting and classifying points of interest based on joint motion using multiple sensor data. The approach, for example, involves determining co-ride data for at least two users based on a joint motion prediction computed using sensor data collected from respective devices associated with the at least two users. The joint motion prediction is computed based on sensor data collected from the respective devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves processing the co-ride data to perform a detection, a classification, or a combination thereof of one or more locations associated with the joint motion prediction.

Abstract: An approach is provided for performing ride-sharing functions based on joint motion using multiple sensor data. The approach, for example, involves retrieving a joint motion prediction indicating whether at least two devices are traveling in a same transportation vehicle. The joint motion prediction is computed based on sensor data collected from the at least two devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves initiating a ride-sharing function for respective users of the at least two devices based on the joint motion prediction.

Abstract: An approach is provided for performing social-networking functions based on joint motion using multiple sensor data. The approach, for example, involves determining a co-ride event between at least two users based on a joint motion prediction computed using sensor data collected from respective devices associated with the at least two users. The joint motion prediction is computed based on sensor data collected from the respective devices using at least one sensor type from among a plurality of sensor types. Each sensor type of the plurality of sensor types is associated with a respective joint motion classifier configured to compute a sensor-type joint motion prediction that is used for generating the joint motion prediction. The approach also involves determining a latent social network between the at least two users based on the co-ride event. The approach further involves providing the latent social network as an output.

Abstract: An approach is provided for detecting joint motion using multiple sensor data. The approach, for example, involves retrieving sensor data at least two devices. The sensor data, for instance, is collected using at least one sensor type from among a plurality of sensor types and wherein, and each sensor type of the plurality of sensor types is associated with a respective joint motion classifier. The approach also involves processing the sensor data using the respective joint motion classifier for said each sensor type of the least one sensor type to compute a respective sensor-type joint motion prediction. The approach further involves processing the respective sensor-type joint motion prediction for said each sensor type using a unified classifier to compute a unified joint motion prediction for the at least two devices. The approach further involves providing the unified joint motion prediction as an output.