Abstract: A system and related methods for management, planning and control of a connected fleet of vehicles. A unique, single integrated platform may be provided for management, planning and control of a connected fleet of vehicles, including fleet planning, in-fleeting operations, vehicle acquisition and provisioning, vehicle assignment, vehicle transfers, vehicle use operations, vehicle servicing, vehicle maintenance and repairs, and de-fleeting operations. Fleet communications can be by cellular, wireless, or low earth orbit satellite communications. Fleet vehicles include a programmable TCU installed in the vehicle and connected, directly or indirectly, to the CAN bus or similar vehicle network, and carries out various operations, including controlling vehicle access. Where outside communications links are not available, access to a vehicle may be through a protected, secure, single-use token stored on a user's computing device upon making a reservation, which is later securely communicated to the vehicle.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: Provided are systems and methods that use artificial intelligence and/or machine learning to dynamically allocate services at different times and at different network edge locations within a Multi-Access Edge (“MEC”) enhanced network based on a multitude of factors that change the priorities of the services at the different times and at the different edge locations. For instance, a MEC controller, controlling the allocation of resources at a particular edge location, may modify the allocation of services at that particular edge location at different times based on time and/or location sensitive events that occur at different times and that relate to different services, changing usage patterns that are derived from prior service utilization, and/or categorization of the services as permanent, time insensitive, or other categories of services with permissions to execute at different times from different edge locations.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A device may receive fiber sensing data identifying vehicles traveling on a roadway associated with a fiber optic network and location data identifying geographical locations of the vehicles traveling on the roadway. The device may process the fiber sensing data, with a machine learning model, to identify a particular vehicle, of the vehicles, that is traveling in a wrong direction on the roadway. The device may process the location data, with the machine learning model, to identify locations of the roadway, a cellular network associated with the roadway, and vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle, and a nearest camera device to the particular vehicle. The device may perform one or more actions based on the locations of the roadway, the cellular network associated with the roadway, and the vehicle devices of the vehicles traveling on the roadway, other than the particular vehicle.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A system and connected user mobile device interface for tracking one or more shuttle buses and providing a visual display thereof along with estimated times of arrival at the connected user's location or at the shuttle bus stop closest to the connected user. The tracking system is a learning-based model that tracks vehicle movement to estimate arrival time at pre-determined geo-fence locations based on historical behavior for similar time periods and conditions.

Abstract: A device, method, and system provide for collecting service parameters associated with application sessions associated with a plurality of MEC clusters; obtaining capability information associated with each of the plurality of MEC clusters; generating service profiles for a set of transport networks based on the service parameters and the capability information, wherein each transport network includes at least one of the plurality of MEC clusters; receiving, from a user equipment (UE) device, a request for a MEC service having a minimum service requirement; and selecting, based on the service profiles, a MEC cluster from the plurality of MEC clusters to provide the MEC service, wherein the selected MEC cluster is included in a first transport network that meets the minimum service requirement.

Abstract: A system and connected user mobile device interface for tracking one or more shuttle buses and providing a visual display thereof along with estimated times of arrival at the connected user's location or at the shuttle bus stop closest to the connected user. The tracking system is a learning-based model that tracks vehicle movement to estimate arrival time at pre-determined geo-fence locations based on historical behavior for similar time periods and conditions.

Abstract: A device may receive, from a user device, a request to activate an extended reality experience. The device may obtain access network information relating to a set of access networks available to the user device and user device information relating to the user device. Based on the access network information and the user device information, the device may determine an access network to use for the extended reality experience. The device may determine, based on the access network, a first portion of the extended reality experience to execute locally or a second portion of the extended reality experience to execute remotely.

Abstract: A system and related methods for management, planning and control of a connected fleet of vehicles. A unique, single integrated platform may be provided for management, planning and control of a connected fleet of vehicles, including fleet planning, in-fleeting operations, vehicle acquisition and provisioning, vehicle assignment, vehicle transfers, vehicle use operations, vehicle servicing, vehicle maintenance and repairs, and de-fleeting operations. Fleet communications can be by cellular, wireless, or low earth orbit satellite communications. Fleet vehicles include a programmable TCU installed in the vehicle and connected, directly or indirectly, to the CAN bus or similar vehicle network, and carries out various operations, including controlling vehicle access. Where outside communications links are not available, access to a vehicle may be through a protected, secure, single-use token stored on a user's computing device upon making a reservation, which is later securely communicated to the vehicle.

Abstract: Provided are systems and methods that use artificial intelligence and/or machine learning to dynamically allocate services at different times and at different network edge locations within a Multi-Access Edge (“MEC”) enhanced network based on a multitude of factors that change the priorities of the services at the different times and at the different edge locations. For instance, a MEC controller, controlling the allocation of resources at a particular edge location, may modify the allocation of services at that particular edge location at different times based on time and/or location sensitive events that occur at different times and that relate to different services, changing usage patterns that are derived from prior service utilization, and/or categorization of the services as permanent, time insensitive, or other categories of services with permissions to execute at different times from different edge locations.

Abstract: A device may receive, from a user device, a request to activate an extended reality experience. The device may obtain access network information relating to a set of access networks available to the user device and user device information relating to the user device. Based on the access network information and the user device information, the device may determine an access network to use for the extended reality experience. The device may determine, based on the access network, a first portion of the extended reality experience to execute locally or a second portion of the extended reality experience to execute remotely.

Abstract: A system and related methods for management of communications and interactions between a user and a connected fleet of vehicles. Fleet vehicle reservations and rental transaction systems are blockchain-based systems, which incorporate customer interaction, including payments through customary payment channels and/or cryptocurrency, through customer personal computers and/or mobile devices. One or more unmanned aerial vehicles (drones) may be used to respond to accidents to fleet vehicles on the road, and document with digital images damage to the fleet vehicle or other vehicles, as well as road conditions and environmental factors.