Abstract: Parking space selection and navigation systems and methods are disclosed. Computing devices are configured to receive a search request from a user device to secure a first parking space, secure a first parking space for the first user device, determine a first value associated with the first parking space and a second value associated with a second parking space based on listing parameters, determine that the second value is greater than the first value by a predefined amount, and, in response to that determination, transmit a message to the user device comprising a prompt configured to secure the second listing and transmitting a second user-selectable message to a second user device, the second user-selectable message comprising a prompt configured to exchange the second parking space with the first with an incentive for the second user device to exchange parking spaces.

Abstract: A computerized vehicle management system, comprising a non-transitory memory; and one or more processors coupled to the non-transitory memory and configured to execute instructions to perform operations comprising capturing, using one or more cameras at a parking lot, first image data associated with a vehicle; capturing, using the one or more cameras at the parking lot, second image data associated with the parking lot; providing the image data associated with the parking lot to a computer vision machine learning model to determine a condition of the parking lot indicating at least dimensions of one or more available parking spaces; determining the condition of the parking lot based on a first output of the computer vision machine learning model.

Abstract: The technologies regarding dynamic image-based parking systems and methods of operation thereof are disclosed. An example method for operating a camera-based parking management system includes: activating an AI-enabled camera covering a parking area responsive to detecting a parking object; capturing a snapshot of the parking area using the AI-enabled camera; transmitting the snapshot to an edge processor to identify parking object(s) shown in the snapshot; determining that the parking object shown in the snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model; responsive to the determining: identifying first one or more characteristics about the parking object that potentially reduce identification accuracy; and identifying second one or more characteristics about the snapshot (e.g.

Abstract: The present disclosure generally relates to blockchain technology and, more specifically, to improved systems and methods of establishing, supporting and securing image-based parking recognition and navigation communications via blockchain systems. In some aspects, the present disclosure provides a process for collecting, using one or more session border controllers (SBCs) and one or more cameras, a set of vehicle data wherein the set of vehicle data comprises one or more unique identification features corresponding to one or more vehicles and wherein the one or more vehicles are located in a parking facility. In some cases, the process can further include steps for establishing, using the one or more SBCs and the one or more cameras, a private blockchain wherein the one or more cameras serve as nodes in the private blockchain and wherein the one or more cameras serve as a gateway for the one or more session border controllers.

Abstract: The technologies regarding dynamic image-based parking systems and methods of operation thereof. An example method for operating a camera-based parking management system includes: activating an AI-enabled camera covering a parking area responsive to detecting a parking object; capturing a snapshot of the parking area using the AI-enabled camera; transmitting the snapshot to an edge processor to identify parking object(s) shown in the snapshot; determining that the parking object shown in the snapshot is not capable of being identified with a predefined degree of certainty based on a machine learning model; responsive to the determining: identifying first one or more characteristics about the parking object that potentially reduce identification accuracy; and identifying second one or more characteristics about the snapshot (e.g.

Abstract: A computing system, includes: a non-transitory memory; processors coupled to the non-transitory memory and configured to execute instructions to perform operations including: detecting a vehicle at a first location, executing instructions local to the first location by one or more edge processors to determine vehicle specific parameters that identify the vehicle; determining the vehicle is associated with a user account stored in a computerized vehicle management system; in response to the determining that the vehicle is associated with the user account, and transmitting instruction to a first display at the first location to present a user-tailored menu generated by using the edge processors to access a machine learning computer model based on data obtaining from the user account.

Abstract: A computing system, includes: a non-transitory memory; processors coupled to the non-transitory memory and configured to execute instructions to perform operations including: detecting a vehicle at a first location, executing instructions local to the first location by one or more edge processors to determine vehicle specific parameters that identify the vehicle; determining the vehicle is associated with a user account stored in a computerized vehicle management system; in response to the determining that the vehicle is associated with the user account, and transmitting instruction to a first display at the first location to present a user-tailored menu generated by using the edge processors to access a machine learning computer model based on data obtaining from the user account.

Abstract: A computerized vehicle management system, comprising a non-transitory memory; and one or more processors coupled to the non-transitory memory and configured to execute instructions to perform operations comprising capturing, using one or more cameras at a parking lot, first image data associated with a vehicle; capturing, using the one or more cameras at the parking lot, second image data associated with the parking lot; providing the image data associated with the parking lot to a computer vision machine learning model to determine a condition of the parking lot indicating at least dimensions of one or more available parking spaces; determining the condition of the parking lot based on a first output of the computer vision machine learning model.

Abstract: Image-based parking recognition and navigation systems and methods are disclosed. An example method includes: obtaining camera data collected by the plurality of cameras, the image data identifying one or more parking locations; analyzing the camera data in accordance with one or more machine learning techniques to identify a plurality of candidate parking location, obtaining, from a smart phone app executed on a mobile user device, a user request to reserve a candidate parking location in the plurality of candidate parking location; determining a location of the candidate parking location based on location data associated with a first camera; identifying a booking method to reserve the candidate parking location based on the location of the candidate parking location; and responsive to obtaining the user request, enabling a user issuing the user request to reserve the candidate parking location through smart phone app executed on the mobile user device.

Abstract: A method for managing vehicles comprises: detecting motion of an object at a first location associated with a geographic area using a motion sensor, the geographic area including one or more parking locations; responsive to detecting the motion of the object at the first location, capturing, using one or more cameras, image data associated with the first location; using one or more edge processors located within a threshold proximity of the geographic area to access a machine learning computer vision model. The one or more edge processors are configured to execute instructions locally to the first location to perform operations comprising: obtaining, at the computer vision model, the image data associated with the object; determining, by the computer vision model, that the object is a vehicle; identifying, by the computer vision model, one or more vehicle specific parameters associated with the vehicle; and determine the one or more vehicle specific parameters that identify the vehicle.

Abstract: A computing system, includes: a non-transitory memory; processors coupled to the non-transitory memory and configured to execute instructions to perform operations including: detecting a vehicle at a first location, executing instructions local to the first location by one or more edge processors to determine vehicle specific parameters that identify the vehicle; determining the vehicle is associated with a user account stored in a computerized vehicle management system; in response to the determining that the vehicle is associated with the user account, and transmitting instruction to a first display at the first location to present a user-tailored menu generated by using the edge processors to access a machine learning computer model based on data obtaining from the user account.

Abstract: A method for managing vehicles comprises: detecting motion of an object at a first location associated with a geographic area using a motion sensor, the geographic area including one or more parking locations; responsive to detecting the motion of the object at the first location, capturing, using one or more cameras, image data associated with the first location; using one or more edge processors located within a threshold proximity of the geographic area to access a machine learning computer vision model. The one or more edge processors are configured to execute instructions locally to the first location to perform operations comprising: obtaining, at the computer vision model, the image data associated with the object; determining, by the computer vision model, that the object is a vehicle; identifying, by the computer vision model, one or more vehicle specific parameters associated with the vehicle; and determine the one or more vehicle specific parameters that identify the vehicle.

Abstract: Image-based parking recognition and navigation systems and methods are disclosed. An example method includes: obtaining camera data collected by the plurality of cameras, the image data identifying one or more parking locations; analyzing the camera data in accordance with one or more machine learning techniques to identify a plurality of candidate parking location; obtaining, from a smart phone app executed on a mobile user device, a user request to reserve a candidate parking location in the plurality of candidate parking location; determining a location of the candidate parking location based on location data associated with a first camera; identifying a booking method to reserve the candidate parking location based on the location of the candidate parking location; and responsive to obtaining the user request, enabling a user issuing the user request to reserve the candidate parking location through smart phone app executed on the mobile user device.

Abstract: Parking space selection and navigation systems and methods are disclosed. Computing devices are configured to receive a search request from a user device to secure a first parking space, secure a first parking space for the first user device, determine a first value associated with the first parking space and a second value associated with a second parking space based on listing parameters, determine that the second value is greater than the first value by a predefined amount, and, in response to that determination, transmit a message to the user device comprising a prompt configured to secure the second listing and transmitting a second user-selectable message to a second user device, the second user-selectable message comprising a prompt configured to exchange the second parking space with the first with an incentive for the second user device to exchange parking spaces.

Abstract: A computing system, includes: a non-transitory memory; processors coupled to the non-transitory memory and configured to execute instructions to perform operations including: detecting a vehicle at a first location, executing instructions local to the first location by one or more edge processors to determine vehicle specific parameters that identify the vehicle; determining the vehicle is associated with a user account stored in a computerized vehicle management system; in response to the determining that the vehicle is associated with the user account, and transmitting instruction to a first display at the first location to present a user-tailored menu generated by using the edge processors to access a machine learning computer model based on data obtaining from the user account.

Abstract: Parking space selection and navigation systems and methods are disclosed. One or more computing devices are configured to receive a search request from a first user device to secure a parking space for a vehicle, which includes one or more listing parameter, secure a first parking space stored in a database based on the search request, and determine a first listing value for the first parking space. The computing device(s) are configured to receive information associated with a second parking space and determine a second listing value for the second parking space. The computing device(s) are configured to determine that the second listing value is greater than the first listing value by a predefined value, transmit a message to the first and second user devices, and automatically transmit information relating to the parking reservation to a navigation application to update a destination associated with the second parking space.

Abstract: Parking space selection and navigation systems and methods are disclosed. One or more computing devices are configured to receive a search request from a first user device to secure a parking space for a vehicle, which includes one or more listing parameter, secure a first parking space stored in a database based on the search request, and determine a first listing value for the first parking space. The computing device(s) are configured to receive information associated with a second parking space and determine a second listing value for the second parking space. The computing device(s) are configured to determine that the second listing value is greater than the first listing value by a predefined value, transmit a message to the first and second user devices, and automatically transmit information relating to the parking reservation to a navigation application to update a destination associated with the second parking space.

Abstract: A computing system, includes: a non-transitory memory; processors coupled to the non-transitory memory and configured to execute instructions to perform operations including: detecting a vehicle at a first location, executing instructions local to the first location by one or more edge processors to determine vehicle specific parameters that identify the vehicle; determining the vehicle is associated with a user account stored in a computerized vehicle management system; in response to the determining that the vehicle is associated with the user account, and transmitting instruction to a first display at the first location to present a user-tailored menu generated by using the edge processors to access a machine learning computer model based on data obtaining from the user account.

Abstract: Parking space selection systems and methods are disclosed. One or more computing devices are configured to determine a first listing value based on one or more listing parameters associated with a first listing, which is secured by a first wireless device. A second listing value is determined based on one or more listing parameters associated with a second listing. The first and second listing value are compared and a difference between them is determined. It is determined whether the difference between the first listing value and the second listing value exceeds a predetermined threshold. In response to the determination that the difference exceeds the predetermined threshold, a user-selectable message is provided on the first wireless device. The user-selectable message includes a prompt configured to secure the second listing. In response to the prompt, the second listing is secured for the first wireless device.