Trajectory prediction using data normalization

Abstract

A method and system for trajectory prediction is provided, the method and system including a plurality of processes comprising: receiving a plurality of observed speed points; processing the plurality of observed speed points corresponding to the observed trajectory using a machine learning technique to generate a plurality of predicted speed points, the machine learning technique being trained to establish a relationship between the plurality of training observed speed points and the plurality of training predicted speed points; identifying a future trajectory based on the plurality of predicted speed points, the plurality of predicted speed points each corresponding to a different segment of a plurality of segments of the future trajectory; determining that a target access control device is within a threshold range of the future trajectory; and executing a process associated with the target access control device.

Description

[Technical Field]

[0001] The present disclosure relates to trajectory prediction using data normalization. [Background technology]

[0002] Trajectory prediction plays an important role in many tasks, such as intelligent access control systems. It is generally defined as predicting the position of a mobile agent (e.g., a person, a vehicle, or a mobile device) at each time step within a given future time interval based on multiple partial trajectories observed over a period of time. Summary of the Invention

[0003] In some aspects, a method is provided, the method including: one or more processors receiving a plurality of observed speed points, each of the plurality of observed speed points corresponding to a different section of a plurality of sections of an observed trajectory; processing the plurality of observed speed points corresponding to the observed trajectory using a machine learning technique to generate a plurality of predicted speed points, the machine learning technique being trained to establish a relationship between a plurality of training observed speed points and a plurality of training predicted speed points; identifying a future trajectory based on the plurality of predicted speed points, each of the plurality of predicted speed points corresponding to a different section of a plurality of sections of the future trajectory; determining that a target access control device is within a threshold range of the future trajectory; and performing processing associated with the target access control device in response to determining that the target access control device is within the threshold range of the future trajectory.

[0004] In some aspects, the plurality of observed speed points include a plurality of acceleration measurements, the plurality of predicted speed points include a plurality of acceleration measurements, the target access control device includes a lock associated with a door, and performing the plurality of operations includes unlocking the door.

[0005] In some aspects, the method includes establishing a wireless communication link between a user's mobile device and the target access control device, exchanging authentication information over the wireless communication link, and performing the process after determining that the user is authorized to access the target access control device based on the authentication information.

[0006] In some aspects, the method includes determining, based on the authentication information, that the user is authorized to access the target access control device before performing the operation, and delaying performing the operation after determining that the user is authorized until the target access control device is determined to be within the threshold range of the future trajectory.

[0007] In some aspects, the wireless communication link includes a Bluetooth Low Energy (BLE) communication protocol and the plurality of observed velocity points are received via an Ultra Wideband (UWB) communication protocol.

[0008] In some embodiments, the target access control device is located indoors. In some aspects, the method includes determining, based on the authentication information, that the user is authorized to access the target access control device before performing the operation, and in response to determining that the target access control device is outside the threshold range of the future trajectory, preventing the operation from being performed after determining that the user is authorized.

[0009] In some embodiments, the machine learning technique comprises a neural network. In some embodiments, the method includes receiving a first data point indicating a first two-dimensional (2D) or three-dimensional (3D) Cartesian coordinate at a first time point, receiving a second data point indicating a second 2D or 3D Cartesian coordinate at a second time point, each of the first and second data points corresponding to a first section of the plurality of sections, and calculating a first observed speed point among the plurality of observed speed points as a function of the difference between the first 2D or 3D Cartesian coordinate and the second 2D or 3D Cartesian coordinate and the difference between the first time point and the second time point.

[0010] In some aspects, the machine learning techniques generate the plurality of predicted speed points independent of a sampling rate of the plurality of observed speed points. In some aspects, the method includes identifying the future trajectory based on the plurality of predicted speed points by determining a sampling rate at which the plurality of observed speed points are received based on a difference between a first timestamp of a first observed speed point among the plurality of observed speed points and a second timestamp of a second observed speed point among the plurality of observed speed points, and calculating three-dimensional Cartesian coordinates of the future trajectory based on the plurality of predicted speed points, a user's current location, and the sampling rate at which the plurality of observed speed points are received.

[0011] In some embodiments, the method includes training the machine learning technique by obtaining a first set of training data including the plurality of training observed speed points and a corresponding first set of the training predicted speed points, processing the plurality of training observed speed points using the machine learning technique to generate a plurality of estimated speed points, calculating a loss based on the deviation between the plurality of estimated speed points and the corresponding first set of training predicted speed points based on a loss function, and updating a plurality of parameters of the machine learning technique based on the calculated loss function.

[0012] In some aspects, the first set of training observation speed points corresponds to a first sampling rate, and the method further includes acquiring a second group of training data including a second set of training observation speed points, the second set of training observation speed points corresponding to a second sampling rate; processing the second set of training observation speed points using the machine learning technique to generate a second plurality of estimated speed points; calculating a second loss based on the deviation between the second plurality of estimated speed points and the corresponding first set of training predicted speed points based on the loss function; and updating parameters of the machine learning technique based on the second loss.

[0013] In some aspects, a system is provided, the system comprising one or more processors coupled to a memory containing non-transitory computer instructions that, when executed by the one or more processors, cause a plurality of processes to be performed, the plurality of processes including: receiving a plurality of observed speed points, each of the plurality of observed speed points corresponding to a different section of a plurality of sections of an observed trajectory; processing the plurality of observed speed points corresponding to the observed trajectory using a machine learning technique to generate a plurality of predicted speed points, the machine learning technique being trained to establish a relationship between a plurality of training observed speed points and a plurality of training predicted speed points; identifying a future trajectory based on the plurality of predicted speed points, each of the plurality of predicted speed points corresponding to a different section of a plurality of sections of the future trajectory; determining that a target access control device is within a threshold range of the future trajectory; and performing a process associated with the target access control device in response to determining that the target access control device is within the threshold range of the future trajectory.

[0014] In some aspects, the plurality of observed speed points include a plurality of acceleration measurements, the plurality of predicted speed points include a plurality of acceleration measurements, the target access control device includes a lock associated with a door, and performing the plurality of operations includes unlocking the door.

[0015] In some embodiments, the plurality of processes further include establishing a wireless communication link between a user's mobile device and the target access control device, exchanging authentication information over the wireless communication link, and performing the processes after determining, based on the authentication information, that the user is authorized to access the target access control device.

[0016] In some aspects, the plurality of operations further includes determining, based on the authentication information, that the user is authorized to access the target access control device before performing the operation, and delaying performing the operation after determining that the user is authorized until it is determined that the target access control device is within the threshold range of the future trajectory.

[0017] In some aspects, a non-transitory computer-readable medium is provided, the non-transitory computer-readable medium including non-transitory computer-readable instructions for performing a plurality of processes, the plurality of processes including receiving a plurality of observed speed points, each of the plurality of observed speed points corresponding to a different section of a plurality of sections of an observed trajectory; processing the plurality of observed speed points corresponding to the observed trajectory using a machine learning technique to generate a plurality of predicted speed points, the machine learning technique being trained to establish a relationship between a plurality of training observed speed points and a plurality of training predicted speed points; identifying a future trajectory based on the plurality of predicted speed points, each of the plurality of predicted speed points corresponding to a different section of a plurality of sections of the future trajectory; determining that a target access control device is within a threshold range of the future trajectory; and executing a process associated with the target access control device in response to determining that the target access control device is within the threshold range of the future trajectory.

[0018] In some aspects, the plurality of observed speed points include a plurality of acceleration measurements, the plurality of predicted speed points include a plurality of acceleration measurements, the target access control device includes a lock associated with a door, and performing the plurality of operations includes unlocking the door.

[0019] In some embodiments, the plurality of processes further include establishing a wireless communication link between a user's mobile device and the target access control device, exchanging authentication information over the wireless communication link, and performing the processes after determining, based on the authentication information, that the user is authorized to access the target access control device. [Brief explanation of the drawings]

[0020] [Figure 1]FIG. 1 is a block diagram of an exemplary access control system, according to some embodiments. [Figure 2] FIG. 2 illustrates an exemplary access control system based on trajectory prediction, according to an exemplary embodiment. [Figure 3] FIG. 3 is a block diagram of an exemplary trajectory prediction system that may be deployed within the access control system of FIG. 1, according to some embodiments. [Figure 4] FIG. 4 is an exemplary database that may be located within the systems of FIGS. 1-3, according to some embodiments. [Figure 5] FIG. 5 is a flowchart illustrating exemplary processes of an access control system according to an exemplary embodiment. [Figure 6] FIG. 6 is a block diagram illustrating an example software architecture that may be used in conjunction with the various hardware architectures described herein. [Figure 7] FIG. 7 is a block diagram illustrating several components of a machine, in accordance with some illustrative embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0021]

[0013] Exemplary methods and systems for trajectory prediction-based access control systems (e.g., physical or logical access control systems) are described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments. However, it will be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without these specific details.

[0022] In a typical access control system, a user carries a physical card or device containing a set of credentials (e.g., authentication information). Such credentials are exchanged with an access control device (e.g., an electronic door lock, a card reader, etc.) when the physical card or device is brought within approximately 20 centimeters of the access control device. At that point, the access control device determines whether the credentials authorize the user to access the access control device, and if so, the access control device grants access (e.g., unlocks the door lock). While such systems generally work well, they require the user to be in close proximity to the access control device in order to activate it. This can result in varying degrees of latency when activating the device, which can be frustrating for users.

[0023] As mobile devices become commonplace, they can be programmed to hold the same set of credentials as commonly used physical cards. These mobile devices can communicate with access control devices over longer distances, such as by using the Bluetooth Low Energy (BLE) communication protocol. For example, a mobile device can transmit credentials over a range of up to 100 meters to exchange with an access control device. In such cases, the access control device can be activated when the user is at a greater distance from the access control device than if the user were using a physical card or device. In this way, when the user finally reaches the access control device, the access control device has already received and authenticated the credentials and granted or denied access to the user. When the user reaches the device, no further action from the user is required to activate the device (e.g., the user does not need to bring a physical card into proximity with the access control device).

[0024] However, these other approaches to exchanging credentials over BLE introduce another problem. Namely, when multiple access control devices are within range of the BLE communication protocol, credentials may be exchanged for devices that the user does not intend to activate. For example, there may be multiple access control devices within range of a user's mobile device to which the user has credentials to access. However, the user may only intend to access a specific device. As another example, a user may pass by a given door or access control device that the user is authorized to access, but may not intend to pass through or activate the given door or access control device. In such cases, identifying the user's trajectory can play an important role in determining which of multiple correct physical access devices to activate and the user's intention with respect to activating such device.

[0025] A typical trajectory prediction system takes a few steps of observed trajectory as input and generates several successive positions in a future timeline. Such typical trajectory prediction systems rely on data acquired at a specific, known sampling frequency. This limits their general applicability because real-world data is not ideal in real-world scenarios, where different types of position sensors emit signals at different frequencies and sensors may have inconsistent sampling rates due to hardware and communication limitations. While some trajectory prediction systems can be trained with multiple sampling frequencies, this further increases their complexity, making them unsuitable for application on mobile devices and platforms.

[0026] The disclosed embodiments provide an intelligent solution for accurately predicting a user's future locations so that an access control system can provide a proactive and seamless experience for users while maintaining high security. The disclosed embodiments provide a trajectory prediction system that predicts a user's trajectory regardless of the sampling rate of input data, observation data, or trajectory points. Based on the predicted trajectory, if a given access control device is within range of the trajectory and the user is authorized for access (e.g., as determined by a long-range exchange of credentials, such as via Bluetooth Low Energy), the given access control device is activated. As an example, a given access control device (e.g., a door lock) can first communicate with the user's mobile device via a specific communication protocol (e.g., Bluetooth Low Energy) to exchange authorization data (e.g., credentials). Then, if the given access control device is determined to be within range of the user's predicted trajectory, the given access control device is instructed to activate (e.g., unlock the door lock). In this way, when a user reaches a given access control device, the given access control device is ready to operate without the user having to bring a physical access card into proximity with the given access control device.

[0027] In some embodiments, disclosed embodiments provide a system and method for performing long-range access control based on trajectory prediction. According to disclosed embodiments, a plurality of observed speed points are received. The plurality of observed speed points corresponding to the observed trajectory are processed by a machine learning technique to generate a plurality of predicted speed points. The machine learning technique is trained to establish a relationship between the training observed speed points and the training predicted speed points. Disclosed embodiments identify a future trajectory based on the plurality of predicted speed points, each corresponding to a different section of a plurality of slices of the future trajectory, and determine whether a target access control device is within a threshold range of the future trajectory. In response to determining that the target access control device is within the threshold range of the future trajectory, disclosed embodiments perform processing associated with the target access control device.

[0028] In some embodiments, disclosed embodiments provide a system and method for performing long-range access control based on trajectory prediction. According to disclosed embodiments, a plurality of observed acceleration points are received. The plurality of observed acceleration points corresponding to the observed trajectory are processed by a machine learning technique to generate a plurality of predicted acceleration points. The machine learning technique is trained to establish a relationship between the training observed acceleration points and the training predicted acceleration points. Disclosed embodiments determine a future trajectory based on the plurality of predicted acceleration points, each of which corresponds to a different segment of a plurality of segments of the future trajectory, and determine whether a target access control device is within a threshold range of the future trajectory. In response to determining that the target access control device is within the threshold range of the future trajectory, disclosed embodiments perform processing associated with the target access control device.

[0029] By training a machine learning technique on multiple speed, acceleration, or velocity points to predict speed or velocity, the disclosed embodiments can predict future trajectories regardless of the sampling rate at which multiple position coordinates of an observed trajectory are acquired or received. That is, the machine learning technique calculates the difference between three-dimensional (3D) coordinates of two 3D positions acquired at different times and divides the difference by the difference in the timestamps at which the 3D coordinates were received. This provides an overall speed for a set of legs along a given observed trajectory. This speed can then be processed to predict future speed. A future trajectory can be determined by deriving future 3D coordinates based on estimates of multiple 3D positions or a known sampling rate. For example, each predicted speed measurement can be multiplied by the sampling rate (e.g., the amount of time between measurements) to identify a distance or predicted segment (e.g., corresponding to the difference between the two predicted 3D coordinates). The predicted segments can be offset by the known current location of the user and can be constructed or combined with additional predicted 3D coordinates to generate a predicted trajectory.

[0030] In an alternative embodiment, the observed location and future trajectory are in two-dimensional space and represented by 2D coordinates. For example, a machine learning technique calculates the difference between the 2D coordinates of two two-dimensional (2D) locations acquired at different times and divides the difference by the difference in the timestamps at which the 2D coordinates were received. This provides a segment speed for the entire set along a given observed trajectory. This speed can then be processed to predict a future speed. The future trajectory can be determined by deriving multiple future 2D coordinates based on an estimated or known sampling rate of multiple 2D locations. For example, each predicted speed measurement can be multiplied by the sampling rate (e.g., the amount of time between measurements) to identify a distance or predicted segment (e.g., corresponding to the difference between two predicted 2D coordinates). The predicted segment can be offset by the known user's current location and constructed or combined with additional predicted 2D coordinates to generate a predicted trajectory.

[0031] 1 is a block diagram illustrating an example system 100 in accordance with various exemplary embodiments. System 100 may be an access control system that includes client devices 120, one or more access control devices 110 that control access to secured assets or resources, such as through lockable doors, and an authentication management system 140 communicatively coupled via a network 130 (e.g., the Internet, BLE, ultra-wideband (UWB) communication protocols, telephone networks, or other wired or wireless communication protocols).

[0032] UWB is a radio frequency (RF) technique that uses short pulses of low power across a wide frequency spectrum. The pulses are on the order of millions of individual pulses per second. The width of the frequency spectrum is typically greater than 500 megahertz, or greater than 20 percent of the arithmetic center frequency.

[0033] UWB can be used for communications, such as by encoding data using time modulation (e.g., pulse position coding), where symbols are designated by pulses in some units of time out of a set of available units of time. Other examples of UWB coding may include amplitude modulation and / or polar modulation. Wideband transmissions tend to be more tolerant of multipath attenuation than carrier-based transmission techniques. Furthermore, because the power of the pulses is weaker at any given frequency, they tend to interfere less with carrier-based communication techniques.

[0034] UWB can be used in radar operations to provide localization with an accuracy of tens of centimeters. Because pulses can vary in absorption and reflection of different frequencies, it can detect both surface and occluded (e.g., covered) features of an object. In some cases, localization provides angle of incidence in addition to range.

[0035] The client device 120 and the multiple access control devices 110 may be communicatively coupled using electronic messages (e.g., packets exchanged via the Internet, BLE, UWB, WiFi Direct, or any other protocol). While FIG. 1 depicts a single access control device 110 and a single client device 120, it is understood that in other embodiments, multiple access control devices 110 and multiple client devices 120 may be included in the system 100. As used herein, the term “client device” may refer to any machine that interfaces to a communications network (such as the network 130) to exchange credentials with the access control device 110, the authentication management system 140, another client device 120, or any other component to obtain access to an asset or resource protected by the access control device 110. In some embodiments, the client device 120 may obtain location information using UWB and calculate speed, velocity, and / or acceleration information about the client device 120's current trajectory. The client device 120 may obtain location information, calculate speed, velocity, and / or acceleration information, and provide such information to the authentication management system 140. In some embodiments, the access control device 110 can obtain location information using UWB and calculate speed, velocity, and / or acceleration information for the current trajectory of a given client device 120 or set of client devices 120. The access control device 110 can obtain location information and calculate speed, velocity, and / or acceleration information and can provide such information to the authentication management system 140.

[0036] In some cases, some or all of the components and functionality of authentication management system 140 may be included on client devices 120 (e.g., any of the machine learning techniques described with respect to authentication management system 140 may be implemented on each client device 120). Any component that performs trajectory prediction in system 100 may be implemented as a standalone component of any one of authentication management system 140, client devices 120, or access control devices 110. Multiple functions of any component that performs trajectory prediction in system 100 may be implemented in a distributed manner across any of authentication management system 140, client devices 120, and / or access control devices 110.

[0037] The client device 120 may be, but is not limited to, a mobile phone, a desktop computer, a laptop, a personal digital assistant (PDA), a smartphone, a wearable device (e.g., a smart watch), a tablet, an ultrabook, a netbook, a laptop, a multiprocessor system, a microprocessor-based or programmable consumer electronics device, or any other communication device that a user may use to access a network.

[0038] The access control device 110 may include an access reader device that is connected to a resource (e.g., a door lock mechanism or a back-end server) and controls the resource (e.g., a door lock mechanism). The resource associated with the access control device 110 may include a door lock, a vehicle ignition system, or any other device that can operate to grant or deny access to a physical component. For example, in the case of a door lock, the access control device 110 can deny access, in which case the door lock remains locked and the door cannot be opened, or the access control device 110 can grant access, in which case the door lock is unlocked and the door can be opened. As another example, in the case of an ignition system, the access control device 110 can deny access, in which case the vehicle ignition system remains disabled and the vehicle cannot be started, or the access control device 110 can grant access, in which case the vehicle ignition is enabled and the vehicle can be started.

[0039] Physical access control covers a range of systems and methods for managing access, e.g., by people, to secured areas or assets. Physical access control includes the identification of authorized users or devices (e.g., vehicles, drones, etc.) and the operation of gates, doors, or other equipment used to secure an area, or the operation of controls, e.g., physical or electronic / software controls, to enable access to secured assets. The access control device 110 may form part of a physical access control system (PACS), which may include readers (e.g., online or offline readers) that may hold authorization data and determine whether multiple credentials (e.g., from credential devices or key devices, such as cards, fobs, or radio frequency identification (RFID) chips in personal electronic devices such as mobile phones) are authorized for actuators or controls (e.g., door locks, door openers, software controls, turning off alarms, etc.), or the PACS may include a host server to which readers and actuators are connected (e.g., via a controller) in a centrally managed configuration. In a centralized configuration, a reader can obtain credentials from a credential or key device and pass those credentials to a PACS host server or headend system. The host server then determines whether the credentials grant authorization to access a secured area or asset and commands actuators or other control mechanisms accordingly. While examples of physical access control are used herein, this disclosure applies equally to logical access control system (LACS) use cases (e.g., logical access to personal electronic devices, passenger identification in transportation services, access and asset control in unmanned checkout stores, etc.).

[0040] For example, wireless access control devices that utilize wireless communication between a reader and a credential or key device can use RFID or personal area network (PAN) technologies, such as IEEE 802.15.1, Bluetooth, BLE, near field communications (NFC), ZigBee, GSM, CDMA, and Wi-Fi. Many of these technologies have several drawbacks to providing a seamless user experience. For example, NFC has a very short range, so credential exchange typically does not occur until the user is in close proximity to a secured area or asset and attempts to gain access. The transfer of the credential to the reader and the response by the reader or host server can take several seconds, resulting in user frustration. Furthermore, the user typically must remove the device, for example, from their pocket, and place it at or near the reader to initiate the transaction.

[0041] On the other hand, BLE devices have a range of tens of meters (e.g., 10–20 meters). Therefore, when a user approaches a reader, credential exchange can occur. However, BLE and many other PAN standards do not provide precise physical tracking of devices (e.g., ranging, location, etc.). Therefore, it can be difficult for a reader to determine without further evidence of intent whether the user's intent is to actually access a secured area or asset. For example, it is problematic if an authorized user simply passes a reader in a hallway and a door is unlocked or opened. Evidence of intent may include touching a door handle, gesturing with a key device, etc. However, this may be a less-than-ideal user experience compared to simply walking up to a reader and being granted access to a secured area without any further action or interaction on the user's part.

[0042] To address one or more of these or other issues, location estimation technology (e.g., using secure UWB ranging) can be used and combined with PAN discovery and key exchange. UWB location estimation technology can be more accurate than some conventional technologies, achieving accuracy, for example, on the order of tens of centimeters. UWB location estimation technology can provide both the range and direction of a credential or key device relative to a reader. This accuracy far exceeds the approximately 10-meter accuracy of technologies such as BLE when readers are not linked. The precision of UWB location estimation can be a useful tool for seamlessly determining a user's intent (e.g., whether the user is attempting to access a secured area or asset or simply passing by) and their current or predicted trajectory. For example, several zones may be defined, e.g., near the reader, at the reader, etc., to understand the user's intent from different perspectives. Additionally or alternatively, tracking accuracy can help provide an accurate model that can identify intent from the user's movement or direction of movement. Thus, the reader can classify the user's movement as, for example, likely approaching the reader or simply walking past.

[0043] When an intent trigger occurs, the reader may act on the credentials exchanged, for example, via PAN technology. In the case of an offline reader, e.g., a reader not connected to a control panel or host server, the reader may directly control an actuator or other control mechanism (e.g., a lock on an unconnected door). In a centralized access control system, the (online) reader may forward the credentials to a control panel or host server to act on.

[0044] Generally, the access control device 110 may include one or more of a memory, a processor, one or more antennas, a communication module, a network interface device, a user interface, and a power source or power circuitry.

[0045] The memory of the access control device 110 can be used in connection with the execution of application programming or instructions by the processor of the access control device 110, and for temporary or long-term storage of program instructions or instruction sets and / or credential or authorization data, such as credential data, credential authorization data, or access control data or instructions. For example, the memory can include executable instructions used by the processor to operate other components of the access control device 110 and / or to make access decisions based on the credential or authorization data. The memory of the access control device 110 can include computer-readable media, which can be any medium that can contain, store, communicate, or transfer data, program code, or instructions used by or in connection with the access control device 110. The computer-readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples of suitable computer-readable media include, but are not limited to, an electrical connection having one or more wires, or a tangible storage medium such as a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a dynamic RAM (DRAM), any solid-state storage device, a common compact disc read-only memory (CD-ROM), or other optical or magnetic storage device. Computer-readable media should not be confused with, but includes, computer-readable storage media, which is intended to cover all physical, non-transitory, or similar embodiments of computer-readable media.

[0046] The processor of the access control device 110 may correspond to one or more computer processing devices or resources. For example, the processor may be provided as silicon, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), any other type of integrated circuit (IC) chip, a collection of IC chips, etc. As a more specific example, the processor may be provided as a microprocessor, a central processing unit (CPU), or multiple microprocessors or CPUs configured to execute an instruction set stored in the internal memory and / or memory of the access control device 110.

[0047] The antenna of the access control device 110 may correspond to one or more antennas and may be configured to provide wireless communication between the access control device 110 and a credential or key device (e.g., client device 120). The antenna may be configured to operate using one or more wireless communication protocols and operating frequencies, including, but not limited to, IEEE 802.15.1, Bluetooth, BLE, NFC, ZigBee, GSM, CDMA, Wi-Fi, RF, UWB, etc. By way of example, the one or more antennas may be one or more RF antennas and thus capable of transmitting / receiving RF signals received / transmitted by a credential or key device having an RF transceiver via free space. In some examples, at least one antenna is an antenna designed or configured to transmit and / or receive UWB signals (referred to herein simply as a “UWB antenna”), such that a reader can communicate with the client device 120 using UWB technology.

[0048] The communication module of the access control device 110 may be configured to communicate with one or more different systems or devices, either remote or local to the access control device 110, such as one or more client devices 120 and / or an authentication management system 140, according to any suitable communication protocol.

[0049] The network interface device of the access control device 110 includes hardware that enables communication with one or more client devices 120 and / or other devices, such as the authentication management system 140, over a communications network, such as the network 130, using any one of several transport protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), a mobile phone network (e.g., a cellular network), a Plain Old Telephone (POTS) network, a wireless data network (e.g., the IEEE 802.11 family of standards referred to as Wi-Fi®, the IEEE 802.16 family of standards referred to as WiMax®, the IEEE 802.15.4 family of standards, and a peer-to-peer (P2P) network, etc. In some examples, a network interface device may include an Ethernet port or other physical jack, a Wi-Fi card, a network interface card (NIC), a cellular interface (e.g., an antenna, filters, and associated circuitry), etc. In some examples, a network interface device may include a single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) network. The wireless communication system may include multiple antennas for wireless communication using at least one of the following techniques:

[0050] The user interface of the access control device 110 may include one or more input devices and / or display devices. Examples of suitable user input devices that may be included in a user interface include, but are not limited to, one or more buttons, a keyboard, a mouse, a touch-sensitive surface, a stylus, a camera, a microphone, etc. Examples of suitable user output devices that may be included in a user interface include, but are not limited to, one or more LEDs, an LED panel, a display screen, a touchscreen, one or more lights, a speaker, etc. It should be understood that a user interface may also include combined user input and user output devices, such as a touch-sensitive display, etc.

[0051] Network 130 may include or operate with an ad-hoc network, an intranet, an extranet, a virtual private network (VPN), a LAN, a wireless network, a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), BLE, UWB, the Internet, a portion of the Internet, a portion of the public switched telephone network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi network, another type of network, or a combination of two or more such networks. For example, the network or portion of the network may comprise a wireless or cellular network, and the connection may be a code division multiple access (CDMA) connection, a global system for mobile communications (GSM) connection, or other type of cellular or wireless connection. In this example, the coupling may implement any of the following: single-carrier radio transmission technology (1xRTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, Third Generation Partnership Project (3GPP) including 3G, fourth generation wireless networks (4G), fifth generation wireless networks (5G), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standards, other protocols defined by various standards bodies, other long-distance data transfer technologies, various types of data transfer technologies, etc.

[0052] In one example, when client device 120 approaches access control device 110 (e.g., comes within range of a BLE communication protocol), client device 120 transmits its credentials over network 130. In some cases, the credentials may be selected from multiple credentials based on the current geographic location of client device 120. For example, multiple credentials each associated with a different geographic location may be stored on client device 120. When client device 120 comes within a certain distance (e.g., within 10 meters) of a geographic location associated with one of the multiple credentials, client device 120 retrieves the associated credential from local memory.

[0053] In one example, the client device 120 provides the credentials directly to the access control device 110. In such a case, the access control device 110 communicates the credentials with the authentication management system 140. The authentication management system 140 of FIG. 1 includes an authentication system 142 and a trajectory prediction system 144. The authentication management system 140 may further include the components described with respect to FIGS. 6 and 7, such as a processor and a memory storing instructions that, when executed by the processor, cause the processor to control functions of the authentication management system 140.

[0054] The authentication management system 140 searches a list of credentials stored in the authentication system 142 to determine whether the received credential matches multiple credentials from a list of credentials authorized to access a secured asset or resource (e.g., a door or secured area) protected by the access control device 110. In response to determining that the received credential is authorized to access the access control device 110, the authentication management system 140 accesses a trajectory prediction system 144 to determine whether the trajectory of the client device 120 is predicted to be within a specified range (e.g., 2 meters) of the access control device 110, as described in more detail below. If the trajectory prediction system 144 indicates to the authentication management system 140 that the client device 120 is predicted to follow a trajectory that is within the specified range of the access control device 110, the authentication management system 140 instructs the access control device 110 to perform an action to grant access for the client device 120 (e.g., instructing the access control device to unlock a door).

[0055] In another example, the client device 120 provides credentials to the authentication management system 140. The authentication management system 140 searches a list of credentials stored in the authentication system 142 to determine whether the received credentials match multiple credentials from a list of credentials authorized to access a secured asset or resource (e.g., a door or secured area) protected by the access control device 110. In response to determining that the received credentials are authorized to access the access control device 110, the authentication management system 140 accesses the trajectory prediction system 144 to determine whether the trajectory of the client device 120 is predicted to be within a specified range (e.g., 2 meters) of the access control device 110, as described in more detail below. When the trajectory prediction system 144 indicates to the authentication management system 140 that the client device 120 is predicted to follow a trajectory that is within a specified range of the access control device 110, the authentication management system 140 instructs the access control device 110 (associated with the received credentials and within the geographic distance of the client device 120) to perform an action that grants access for the client device 120 (e.g., instructing the access control device to unlock a door).

[0056] The trajectory prediction system 144 trains machine learning techniques implemented by the authentication management system 140 to predict the speed, velocity, and / or acceleration of the client device 120 based on a set or collection of observed speed, velocity, and / or acceleration measurements. The trajectory prediction system 144 applies a function to the predicted speed, velocity, and / or acceleration to generate a predicted trajectory or path along which the client device 120 is predicted to travel.

[0057] The trajectory prediction system 144 receives a plurality of training observed speed, velocity, and / or acceleration measurements and a corresponding set of training prediction measurements (e.g., observed speed, velocity, and / or acceleration measurements following one or more speed, velocity, and / or acceleration measurements). As described in more detail in connection with FIG. 3 , the trajectory prediction system 144 processes pairs of training observed speed, velocity, and / or acceleration measurements and ground-truth speed measurements (for a plurality of positions following the observed speed, velocity, and / or acceleration measurements) to train a machine learning technique. For example, the machine learning technique (e.g., a neural network) estimates an estimated speed, velocity, and / or acceleration for a given observed speed, velocity, and / or acceleration. The neural network compares the estimated speed, velocity, and / or acceleration with the corresponding ground-truth speed, velocity, and / or acceleration measurements to generate an error. Using the loss function and based on the error, the neural network is updated and applied to another set of training observed speed measurements and ground truth speed measurements. The parameters of the neural network are again adjusted, and if the loss function meets a stopping criterion, the neural network is trained and utilized by the trajectory prediction system 144 to predict speed, velocity, and / or acceleration measurements given a set of observed speed, velocity, and / or acceleration measurements.The machine learning techniques described herein include neural networks such as long-short-term memory neural networks (LSTMs), autoencoders, variational autoencoders, conditioned variational autoencoders, convolutional neural networks, radial basis networks, deep feedforward networks, recurrent neural networks, gated recurrent units, denoising autoencoders, sparse autoencoders, Markov chains, Hopfield networks, Boltzmann machines, deep belief networks, deep convolutional networks, deconvolutional neural networks, generative adversarial networks, liquid state machines, extreme learning machines, echo state networks, deep residual networks, support vector machines, Korhonen networks, or any combination thereof.

[0058] For example, the trajectory prediction system 144 receives one or more observed speed, velocity, and / or acceleration measurements from the client device 120. For example, the trajectory prediction system 144 may receive the observed speed, velocity, and / or acceleration (

[0059]

number

[0060]

number

[0061]

number

[0062]

number

[0063] In one example, client device 120 (or alternatively, access device 110) uses UWB (optionally in combination with one or more other location measurement techniques) to calculate current 3D coordinates of client device 120. In some cases, client device 120 (or alternatively, access device 110) calculates the 3D coordinates using UWB. In some cases, after collecting sequential 3D coordinates, client device 120 (or alternatively, access device 110) applies a denoising and / or smoothing technique to the sequence of 3D coordinates before converting them into multiple speed features. Such denoising and / or smoothing techniques may include: a Kalman filter, a moving average, a polynomial regression, locally weighted scatterplot smoothing, and / or Gaussian smoothing.

[0064] After collecting two consecutive 3D coordinates, each associated with a respective timestamp (and optionally denoising and smoothing the 3D coordinates), client device 120 (or alternatively, access device 110) calculates the difference between the two 3D coordinates (e.g., along the x-axis, y-axis, and z-axis).

[0065]

number

[0066] The client device 120 calculates additional speed, velocity, and / or acceleration measurements for additional consecutive 3D coordinates in the set of 3D coordinates and provides these speed, velocity, and / or acceleration measurements to the trajectory prediction system 144 as the observed speed, velocity, and / or acceleration along the observed trajectory. In some cases, the client device 120 also provides the average sampling rate (e.g., the average of the timestamp differences used to calculate each speed measurement) to the trajectory prediction system 144. The trajectory prediction system 144 calculates the observed speed measurements (e.g.,

[0067]

number

[0068]

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[0069] Once the trajectory prediction system 144 predicts a speed, velocity, and / or acceleration measurement, or a set of speed, velocity, and / or acceleration measurements, the trajectory prediction system 144 obtains a sampling rate from the client device 120 (or alternatively, the access device 110). The sampling rate indicates the length of time between collection of each 3D coordinate observed by the client device 120. In some cases, the sampling rate is fixed and known, while in other cases, the sampling rate is calculated in real time by the client device 120 as the 3D coordinates are collected. The trajectory prediction system 144 calculates corresponding future segments of the predicted path based on the predicted speed, velocity, and / or acceleration measurements and the sampling rate obtained from the client device 120. For example, the trajectory prediction system 144 multiplies a first predicted speed, velocity, and / or acceleration measurement by the sampling rate to obtain a predicted 3D distance traveled or a predicted segment or section of the predicted trajectory. The trajectory prediction system 144 then multiplies the second predicted speed measurement by the sampling rate to obtain a second predicted 3D distance traveled or a predicted segment or section of the predicted trajectory. The trajectory prediction system 144 combines the multiple predicted segments or sections together to form a predicted path or trajectory along which the client device 120 is predicted to travel.

[0070] In an alternative embodiment, once the trajectory prediction system 144 predicts a speed, velocity, and / or acceleration measurement, or a set of speed, velocity, and / or acceleration measurements, the trajectory prediction system 144 obtains a sampling rate from the client device 120 (or alternatively, the access device 110). The sampling rate indicates the length of time between collection of each 2D coordinate observed by the client device 120. In some cases, the sampling rate is fixed and known, while in other cases, the sampling rate is calculated in real time by the client device 120 as the 2D coordinates are collected. The trajectory prediction system 144 calculates corresponding future segments of the predicted path based on the predicted speed, velocity, and / or acceleration measurements and the sampling rate obtained from the client device 120. For example, the trajectory prediction system 144 multiplies a first predicted speed, velocity, and / or acceleration measurement by the sampling rate to obtain a predicted 2D distance traveled or a predicted segment or section of the predicted trajectory. The trajectory prediction system 144 then multiplies the second predicted speed measurement by the sampling rate to obtain a second predicted 2D distance traveled or a predicted segment or section of the predicted trajectory. The trajectory prediction system 144 combines multiple predicted segments or sections together to form a predicted path or trajectory along which the client device 120 is predicted to travel.

[0071] The trajectory prediction system 144 obtains a specified range of activation or operation of the access control device 110. In one example, when the trajectory prediction system 144 is implemented on a server remote from the access control device 110, the trajectory prediction system 144 obtains a unique identifier of the access control device 110 and searches one or more access control device range(s) 430 stored in the database 400 ( FIG. 4 ) to identify and retrieve a range associated with the unique identifier of the access control device 110. Different access control devices 110 or types of access control devices 110 may be associated with different ranges of activation or operation, each stored with its respective unique identifier in one or more access control device range(s) 430. In some instances, the one or more access control device ranges 430 store a device type along with each range. In such situations, the device type, rather than the unique identifier, is used to retrieve the associated range from the one or more access control device ranges 430. The trajectory prediction system 144 determines whether the predicted trajectory falls within a specified range of the access control device 110. If so, the trajectory prediction system 144 instructs the authentication management system 140 to activate or operate the access control device 110 to grant access for the client device 120.

[0072] In another example, the trajectory prediction system 144 is implemented locally on the access control device 110. In such a case, the access control device 110 is hard programmed with a corresponding range of activations (e.g., a range stored in the access control device range 430 for the access control device 110). The trajectory prediction system 144 implemented on the access control device 110 determines whether the predicted trajectory falls within the hard-coded range. If so, the trajectory prediction system 144 causes the access control device 110 to grant access for the client device 120. In another example, the trajectory prediction system 144 is implemented on the client device 120 and provides the trajectory prediction to the access control device 110. The access control device 110 then determines whether the client device 120 is within the range associated with the access control device 110 and grants / denies access for the client device 120.

[0073] In some cases, the trajectory prediction system 144 does not have access to range information and simply provides a predicted trajectory or set of predicted trajectories to the authentication management system 140, the client device 120, and / or the access control device 110. These devices then collectively or individually make a determination as to whether the predicted trajectory is within a threshold range.

[0074] 2 shows an example access control system 200 based on trajectory prediction, according to an example embodiment. For example, a user 210 may carry a client device 120 (not shown), such as a mobile device or phone. The client device 120 may collect a set of observed 3D coordinates 230. The client device 120 may calculate respective speed measurements for each pair of adjacent 3D coordinates based on respective timestamps of the coordinates. In another embodiment, the speed information may be measured by the access control device 110 instead of or in addition to the client device 120.

[0075] The client device 120 may determine that two access control devices 220 and 222 are within a specified range of the client device 120. For example, each of the access control devices 220 and 222 is within range of BLE communication with the client device 120. In response, the client device 120 retrieves credentials for both the access control devices 220 and 222 and transmits the credentials to the authentication management system 140. The authentication management system 140 determines that the client device 120 is authorized to access both the access control devices 220 and 222. In response to determining that access is authorized, the authentication management system 140 delays granting access to a particular one of the access control devices 220 or 222 until it determines that the client device 120 is moving along a predicted trajectory that is within a specified range 250 of the respective access control device 220 or 222.

[0076] In another example, there may be a single access control device 110 that secures access to an area protected by a single access control device 110. In such a case, the user's intent to enter the secured area is determined before instructing the access control device 110 to grant access for a given client device 120. Specifically, a determination is made as to whether the user's predicted trajectory will fall within range of the access control device 110 before instructing the access control device 110 to grant access for the client device 120.

[0077] For example, the client device 120 provides observed speed, velocity, and / or acceleration measurements to the trajectory prediction system 144. The trajectory prediction system 144 predicts speed, velocity, and / or acceleration measurements from the observed speed, velocity, and / or acceleration measurements. The trajectory prediction system 144 then calculates a predicted trajectory 240 along which the client device 120 is predicted to travel based on the sampling rate or estimated sampling rate of the client device 120 and / or the access control device 110. In response to determining that the predicted trajectory 240 is within range of the first access control device 220, the trajectory prediction system 144 instructs the authentication management system 140 to cause the first access control device 220 to grant access for the client device 120 (e.g., the first access control device 220 is instructed to perform an action such as unlocking an electronic door lock). In response to determining that the predicted trajectory 240 does not fall within the range of the second access control device 222, the trajectory prediction system 144 instructs the authentication management system 140 to cause the second access control device 222 to deny access for the client device 120 (e.g., the second access control device 222 is instructed to remain locked even if multiple credentials of the client device 120 are authorized to access the second access control device 222).

[0078] 3 is a block diagram 300 of an exemplary trajectory prediction system 144 that may be deployed within the system of FIG. 1 , according to some embodiments. Training input 310 includes model parameters 312 and training data 320, which may include paired training data sets 322 (e.g., input-output training pairs) and constraints 326. The model parameters 312 include or provide parameters or coefficients of a corresponding one of the machine learning models. During training, these parameters 312 are adapted based on the input-output training pairs of the training data 320. After the parameters 312 are adapted (post-training), the parameters are used by trained models 360 to run the trained machine learning (ML) models on a set of new data 370.

[0079] The training data 320 includes multiple constraints 326 that may define constraints for a given trajectory. The paired training data 320 may include multiple sets of input-output pairs 322, such as pairs of multiple training observed speed, velocity, and / or acceleration measurements and corresponding training future speed, velocity, and / or acceleration measurements (ground truth speed, velocity, and / or acceleration measurements). The ground truth speed, velocity, and / or acceleration measurements indicate actual measured speed, velocity, and / or acceleration at one or more future time points that follow observed speed, velocity, and / or acceleration measured at multiple previous time points. For example, observed speed measurements can be obtained at a first time point for a first segment of a path. Ground truth speed measurements indicate actual observed speed, velocity, and / or acceleration measured at a second time point for a second segment that follows the first segment. Some components of the training input 310 may be stored separately at a different off-site facility or facilities than other components of the training input 310.

[0080] Training one or more machine learning models 330 trains one or more machine learning techniques based on the sets of input-output pairs of the paired training data 322. For example, training model 330 may train the ML model parameters 312 by minimizing a loss function based on one or more ground truth speed measurements. In particular, the ML model may be applied to a training set of observed speed, velocity, and / or acceleration measurements following an observed path or trajectory to estimate the speed measurements following a future path or trajectory. In some implementations, a derivative of the loss function is calculated based on a comparison of the estimated speed measurements with the ground truth speed measurements, and the parameters of the ML model are updated based on the calculated derivative of the loss function.

[0081] By minimizing the loss function for multiple sets of training data trains, the model parameters 312 of the corresponding multiple ML models are adapted or optimized. In this manner, the ML models are trained to establish a relationship between multiple training observed speed, velocity, and / or acceleration measurements and a corresponding multiple predicted speed, velocity, and / or acceleration measurements.

[0082] In one implementation, the ML model is trained according to supervised learning techniques to estimate multiple speed measurements from multiple training speed measurements. In such a case, to train the ML model, multiple training observed speed, velocity, and / or acceleration measurements are retrieved along with their corresponding training predicted or estimated speed, velocity, and / or acceleration measurements. For example, the training observed speed, velocity, and / or acceleration measurements are retrieved from training observed speed points and predicted speed points 410 stored in database 400 (FIG. 4). The ML model is applied to a first group of training speed, velocity, and / or acceleration measurements to estimate a given set of speed, velocity, and / or acceleration measurements. The group of training speed measurements can be used to train the ML model using the same multiple parameters of the ML model, and can be the entire range of training speed, velocity, and / or acceleration measurements from a particular training speed, velocity, and / or acceleration measurement. In some implementations, the output or results of the ML model are used to calculate or predict a first speed, velocity, and / or acceleration measurement and predicted 3D coordinates of the predicted trajectory based on a known or calculated sampling rate.

[0083] The estimated speed, velocity, and / or acceleration measurements are applied to a loss function, and a gradient or derivative of the loss function is calculated based on expected or ground truth speed, velocity, and / or acceleration measurements. Updated parameters of the ML model are calculated based on the gradient or derivative of the loss function. For example, the parameters of the ML model are included in the trained machine learning techniques 420 of the database 400. The ML model is then applied to a second set of training speed measurements using the updated parameters to again estimate a given set of speed, velocity, and / or acceleration measurements and apply the speed, velocity, and / or acceleration measurements to the loss function for comparison with their corresponding ground truth speed, velocity, and / or acceleration measurements. The parameters of the ML model are again updated, and this iteration of the training process continues for a specified number of iterations or epochs, or until a given convergence criterion is met.

[0084] After the machine learning model is trained, using the model 350 refers to applying the model to new data 370 that may be received, including one or more speed, velocity, and / or acceleration measurements. The trained machine learning techniques can be applied to the new data 370 to generate generated results 380 that include predicted speed, velocity, and / or acceleration measurements that follow a predicted path or trajectory.

[0085] 5 is a flowchart illustrating an example process 500 of access control system 100, according to an example embodiment. Process 500 may be embodied in computer-readable instructions executed by one or more processors, such that the operations of process 500 may be performed in part or in whole by multiple functional components of system 100, and therefore process 500 is described below by way of example with reference thereto. However, in other embodiments, at least some of the operations of process 500 may be deployed on various other hardware configurations. Some or all of the operations of process 500 may be parallel, out of order, or omitted entirely.

[0086] In operation 501, the authentication management system 140 receives a plurality of observed speed points, each of which corresponds to a different section of a plurality of sections of the observed trajectory. For example, the authentication management system 140 receives a set of speed points calculated by the client device 120. The client device 120 calculates each speed point as the difference in 3D between respective adjacent 3D coordinates divided by the difference between their respective timestamps.

[0087] In some embodiments, the client device 120 or the authentication management system 140 determines that a sequence of speed, velocity, or acceleration measurements is missing one or more data points. Specifically, the client device 120 or the authentication management system 140 can determine an average sampling interval used to generate the observed speed points. For example, the client device 120 or the authentication management system 140 can determine that the plurality of observed speed points includes a sequence of seven observed speed points. A portion of the plurality of observed speed points (e.g., six of the seven) may have been calculated based on a first sampling interval (e.g., the difference between two timestamps used to calculate the speed measurements may be a first value), and another portion (e.g., one of the seven) may have been calculated based on a second sampling interval that is greater than or twice the first sampling interval. In this case, the client device 120 or the authentication management system 140 can determine that a particular speed point is missing from the sequence due to the missing timestamp that resulted in the larger sampling interval. In such a situation, the client device 120 or the authentication management system 140 can perform an interpolation technique (such as linear interpolation, polynomial interpolation, spline interpolation, or a Gaussian process) to fill in the missing data and increase the number of velocity measurements by one or more interpolated velocity measurements. This results in a sequence of velocity measurements including a total of eight measurements (if seven were observed) to make predictions about future trajectories. This interpolated sequence of velocity measurements can be provided to the authentication management system 140 to predict velocity measurements. Similar techniques can be applied to generate a sequence of velocity or acceleration measurements.

[0088] In operation 502, the authentication management system 140 processes the plurality of observed speed points corresponding to the observed trajectory using a machine learning technique to generate a plurality of predicted speed points, the machine learning technique being trained to establish a relationship between the plurality of training observed speed points and the plurality of training predicted speed points. For example, the trajectory prediction system 144 applies a trained machine learning model to a set of speed points received from the client device 120 to predict a plurality of future speed points.

[0089] In operation 503, the authentication management system 140 determines a future trajectory based on a plurality of predicted speed points, each of which corresponds to a different segment of a plurality of segments of the future trajectory. For example, the trajectory prediction system 144 multiplies the plurality of predicted speed measurements by a calculated, estimated, or known sampling rate to derive future 3D coordinates or a set of future 3D segments of the predicted path.

[0090] In operation 504, the authentication management system 140 determines whether the target access control device is within a threshold range of a future trajectory. For example, the authentication management system 140 obtains a range of access control devices 110 that are within a certain geographic distance (e.g., or within a BLE communication protocol range) of the client device 120. The authentication management system 140 determines whether the future trajectory of the client device 120 falls within the obtained range.

[0091] In operation 505, the authentication management system 140 performs an operation associated with the target access control device 110 in response to determining that the target access control device 110 is within a threshold range of a future trajectory. For example, the authentication management system 140 instructs the access control device 110 to grant access to the client device 120 (e.g., by unlocking an electronic door lock). In some cases, the authentication management system 140 bypasses the access control device 110 and takes direct control of locked or secured resources.

[0092] FIG. 6 is a block diagram illustrating an exemplary software architecture 606 that may be used in conjunction with the various hardware architectures described herein. FIG. 6 is a non-limiting example of a software architecture, and it will be understood that many other architectures may be implemented to enable the functionality described herein. The software architecture 606 may execute on hardware such as the machine 700 of FIG. 7 , which includes, among other things, a processor 704, a memory 714, and input / output (I / O) components 718. A representative hardware layer 652 is shown, which may represent, for example, the machine 700 of FIG. 7 . The representative hardware layer 652 includes a processing unit 654 having associated executable instructions 604. The executable instructions 604 represent executable instructions of the software architecture 606, including implementations of the methods, components, etc. described herein. The hardware layer 652 also includes a memory and / or storage device memory / storage 656 that also has the executable instructions 604. The hardware layer 652 may also include other hardware 658. The software architecture 606 may be deployed in any one or more of the components shown in FIG.

[0093] In the example architecture of FIG. 6 , the software architecture 606 can be conceptualized as a stack of layers, with each layer providing specific functionality. For example, the software architecture 606 can include multiple layers, such as an operating system 602, multiple libraries 620, multiple frameworks / middleware 618, multiple applications 616, and a presentation layer 614. In operation, the multiple applications 616 and / or other components within those layers can invoke API calls 608 through the software stack and receive messages 612 in response to the API calls 608. The illustrated multiple layers are representative in nature, and not all software architectures have all layers. For example, some mobile or dedicated operating systems may not provide multiple frameworks / middleware 618, while others may provide such layers. Other software architectures may include additional or different layers.

[0094] The operating system 602 may manage multiple hardware resources and provide multiple common services. The operating system 602 may include, for example, a kernel 622, multiple services 624, and multiple drivers 626. The kernel 622 may act as an abstraction layer between the hardware layer and other software layers. For example, the kernel 622 may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security configuration, etc. The multiple services 624 may provide other common services to the other software layers. The multiple drivers 626 are responsible for controlling or interfacing with basic hardware. For example, the multiple drivers 626 may include a display driver, a camera driver, a BLE driver, a UWB driver, a Bluetooth driver, a flash memory driver, a serial communication driver (e.g., a Universal Serial Bus (USB) driver), a Wi-Fi driver, an audio driver, a power management driver, etc., depending on the hardware configuration.

[0095] The libraries 620 provide a common infrastructure used by the application 616 and / or other components and / or layers. The libraries 620 provide functions that allow other software components to perform tasks more easily than by directly interfacing with the underlying operating system 602 functions (e.g., the kernel 622, the services 624, and / or the drivers 626). The libraries 620 may include a system library 644 (e.g., the C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, etc. Additionally, the libraries 620 may include API libraries 646 such as a media library (e.g., a library that supports the presentation and manipulation of various media formats such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.), a graphics library (e.g., an OpenGL framework that may be used to render two-dimensional and three-dimensional graphical content on a display), a database library (e.g., SQLite, which may provide various relational database functions), a web library (e.g., WebKit, which may provide web browsing functions), etc. The libraries 620 may also include a wide variety of other libraries 648 to provide many other APIs to the applications 616 and other software components / devices.

[0096] The frameworks / middleware 618 (sometimes referred to as middleware) provide a higher-level common infrastructure that can be used by the applications 616 and / or other software components / devices. For example, the frameworks / middleware 618 may provide various graphic user interface functionality, high-level resource management, high-level location services, etc. The frameworks / middleware 618 may provide a wide range of other APIs, some of which may be specific to a particular operating system 602 or platform, that can be utilized by the applications 616 and / or other software components / devices.

[0097] The plurality of applications 616 includes built-in applications 638 and / or third-party applications 640. Examples of representative built-in applications 638 may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and / or a game application. The third-party applications 640 may include applications developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of a particular platform, and may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS™ Phone, or other mobile operating systems. The third-party applications 640 may invoke API calls 608 provided by a mobile operating system (such as operating system 602) to enable the functionality described herein.

[0098] Applications 616 may use built-in operating system functionality (e.g., kernel 622, services 624, and / or drivers 626), libraries 620, and frameworks / middleware 618 to create a UI for interacting with users of the system. Alternatively or additionally, in some systems, user interaction may occur through a presentation layer, such as presentation layer 614. In these systems, application / component "logic" can be separated from the aspects of the application / component that interact with the user.

[0099] 7 is a block diagram illustrating components of a machine 700 capable of reading instructions from a machine-readable medium (e.g., a machine-readable storage medium) and performing any one or more of the methodologies described herein, according to some exemplary embodiments. Specifically, FIG. 7 illustrates a schematic diagram of the machine 700 in the exemplary form of a computer system within which instructions 710 (e.g., software, programs, applications, applets, apps, or other executable code) may be executed to cause the machine 700 to perform any one or more of the methodologies discussed herein.

[0100] Thus, the instructions 710 can be used to implement the devices or components described herein. The instructions 710 transform a general, unprogrammed machine 700 into a specific machine 700 programmed to perform the functions described and illustrated in the manner described. In alternative embodiments, the machine 700 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked arrangement, the machine 700 may operate in the capacity of a server or client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 700 may include, but is not limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, an STB, a PDA, an entertainment media system, a mobile phone, a smartphone, a mobile device, a wearable device (e.g., a smart watch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of sequentially or otherwise executing the instructions 710 that specify operations to be performed by the machine 700. Additionally, although only a single machine 700 is illustrated, the term "machine" is also intended to include a collection of machines that individually or jointly execute instructions 710 to perform any one or more of the methodologies described herein.

[0101] Machine 700 may include processor 704, memory / storage 706, and I / O components 718, which may be configured to communicate with each other via bus 702, etc. In one embodiment, processor 704 (e.g., a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a radio frequency integrated circuit (RFIC), another processor, or any suitable combination thereof) may include processor 708 and processor 712, which may execute instructions 710, for example. The term "processor" is intended to include multi-core processor 704, which may include two or more independent processors (sometimes referred to as "cores") capable of simultaneously executing instructions. While FIG. 7 shows multiple processors 704, machine 700 may include a single processor with a single core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiple cores, or any combination thereof.

[0102] Memory / storage 706 may include a memory 714, such as a main memory or other memory storage, a database 710, and a storage unit 716, all of which are accessible to processor 704, such as via bus 702. Storage unit 716 and memory 714 store instructions 710 that embody any one or more of the methods or functions described herein. Also, instructions 710 may reside, completely or partially, within memory 714, within storage unit 716, within at least one of processors 704 (e.g., within a processor's cache memory), or any suitable combination thereof during their execution by machine 700. Thus, memory 714, storage unit 716, and the memory of processor 704 are examples of machine-readable media.

[0103] I / O components 718 may include a wide variety of components for receiving input, providing output, generating output, transmitting information, exchanging information, capturing measurements, etc. The particular I / O components 718 included in a particular machine will depend on the type of machine 700. For example, a portable device such as a mobile phone will likely include a touch input device or other such input mechanism, while a headless server machine will likely not include such a touch input device. It will be understood that I / O components 718 may include many other components not shown in FIG. 7 . I / O components 718 are grouped according to function merely to simplify the following description, and this grouping is in no way limiting. In various embodiments, I / O components 718 may include output components 726 and input components 728. Output components 726 may include visual components (e.g., a display such as a plasma display panel (PDP), LED display, LCD, projector, or cathode ray tube (CRT)), auditory components (e.g., speakers), tactile components (e.g., vibration motors, resistive mechanisms), other signal generators, etc. Input components 728 may include alphanumeric input components (e.g., a keyboard, a touchscreen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input component), point-based input components (e.g., a mouse, touchpad, trackball, joystick, motion sensor, or another pointing device), tactile input components (e.g., physical buttons, a touchscreen that provides the position and / or force of a touch or touch gesture, or other tactile input component), audio input components (e.g., a microphone), etc.

[0104] In further embodiments, the I / O component 718 may include a biometric component 739, a motion component 734, an environmental component 736, or a position component 738, among a wide variety of other components. For example, the biometric component 739 may include components for detecting facial expressions (e.g., hand expressions, facial expressions, vocal expressions, gestures, or eye tracking), measuring biosignals (e.g., blood pressure, heart rate, body temperature, sweat, or brain waves), identifying people (e.g., voice identification, retinal identification, face identification, fingerprint identification, or brainwave-based identification), etc. The motion component 734 may include an acceleration sensor component (e.g., an accelerometer), a gravity sensor component, a rotation sensor component (e.g., a gyroscope), etc. The environmental components 736 may include, for example, a lighting sensor component (e.g., a light meter), a temperature sensor component (e.g., one or more thermometers that detect ambient temperature), a humidity sensor component, a pressure sensor component (e.g., a barometer), an acoustic sensor component (e.g., one or more microphones that detect background noise), a proximity sensor component (e.g., an infrared sensor that detects nearby objects), a gas sensor (e.g., a gas detection sensor that detects concentrations of harmful gases or measures pollutants in the air for safety purposes), or other components that may provide an indication, measurement, or signal corresponding to the surrounding physical environment. The position component 738 may include a location sensor component (e.g., a GPS receiver component), an altitude sensor component (e.g., an altimeter or barometer that detects air pressure from which altitude can be derived), an orientation sensor component (e.g., a magnetometer), etc.

[0105] Communications may be achieved using a wide variety of technologies. I / O component 718 may include a communications component 740 operable to couple machine 700 to network 737 or device 729 via coupling 724 and coupling 722, respectively. For example, communications component 740 may include a network interface component or another suitable device for interfacing with network 737. In further embodiments, communications component 740 may include a wired communications component, a wireless communications component, a cellular communications component, a near-field communications (NFC) component, a Bluetooth® component (e.g., Bluetooth Low Energy), a Wi-Fi® component, and other communications components that provide communications via other modalities. Device 729 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device connected via USB).

[0106] Further, the communications component 740 may detect an identifier or may include a component operable to detect an identifier. For example, the communications component 740 may include an RFID tag reader component, an NFC smart tag detection component, an optical reader component (e.g., an optical sensor for detecting one-dimensional barcodes such as Universal Product Code (UPC) barcodes, multi-dimensional barcodes such as QR (Quick Response) codes, Aztec codes, Data Matrix codes, Dataglyph codes, MaxiCode codes, PDF417 codes, Ultra Code codes, UCC RSS-2D barcodes, and other optical codes), or an acoustic detection component (e.g., a microphone for identifying tagged audio signals). Additionally, various information may be derived via the communications component 740, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location by detection of NFC beacon signals that may indicate a particular location, etc.

[0107] <Terminology> "Carrier signal" in this context refers to any intangible medium capable of storing, encoding, or carrying transitory or non-transitory instructions for execution by a machine, as well as including digital or analog communication signals or other intangible media for facilitating communication of such instructions. Transitory or non-transitory instructions may be sent or received over a network using any one of a number of well-known transfer protocols using a transmission medium via a network interface device.

[0108] A "client device," in this context, refers to any machine that interfaces with a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, PDA, smartphone, tablet, ultrabook, netbook, laptop, multiprocessor system, microprocessor-based or programmable consumer electronics, game console, set-top box, or any other communications device that a user may use to access a network.

[0109] A "communications network" in this context refers to one or more portions of a network, which may be an ad-hoc network, an intranet, an extranet, a VPN, a LAN, a BLE network, a UWB network, a WLAN, a WAN, a WWAN, a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the PSTN, a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi network, another type of network, or a combination of two or more such networks. For example, a network or portion of a network may comprise a wireless or cellular network, and the connection may be a code division multiple access (CDMA) connection, a global system for mobile communications (GSM) connection, or other type of cellular or wireless connection. In this example, the coupling may implement any of single-carrier radio transmission technology (1xRTT), Evolutionary Data Optimization (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data Rates for GSM Evolution (EDGE) technology, Third Generation Partnership Project (3GPP) including 3G, Fourth Generation Wireless Networks (4G), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standards, protocols defined by various standards bodies, other long-distance data transfer technologies, various types of data transfer technologies, etc.

[0110] In this context, "machine-readable medium" refers to a component, device, or other tangible medium capable of temporarily or permanently storing instructions and data, and may comprise, but is not limited to, RAM, ROM, buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., erasable programmable read-only memory (EEPROM)), and / or any suitable combination thereof. The term "machine-readable medium" should be interpreted to comprise a single medium or multiple media capable of storing instructions (e.g., centralized or distributed databases or associated caches and servers). The term "machine-readable medium" should also be considered to comprise any medium, or combination of media, capable of storing instructions (e.g., code) for execution by a machine, such that the instructions, when executed by one or more processors of the machine, cause the machine to perform any one or more of the methodologies described herein. Thus, "machine-readable medium" refers to a single storage device or device, as well as a "cloud-based" storage system or storage network comprising multiple storage devices or devices. The term "machine-readable medium" excludes signals themselves.

[0111] A "component" in this context refers to a device, physical entity, or logic with boundaries defined by function or subroutine calls, branch points, application programming interface (API), or other techniques that provide partitioning or modularization of specific processing or control functions. Components can be combined through interfaces with other components to perform machine processing. A component may be a packaged functional hardware unit designed for use with other components and may be part of a program that typically performs specific functions among related functions. A component may constitute either a software component (e.g., code embodied in a machine-readable medium) or a hardware component. A "hardware component" is a tangible unit capable of performing specific operations and may be configured or arranged in a specific physical manner. In various exemplary embodiments, one or more computer systems (e.g., standalone computer systems, client computer systems, or server computer systems) or one or more hardware components of a computer system (e.g., a processor or group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform specific operations as described herein.

[0112] A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may comprise dedicated circuitry or logic permanently configured to perform specific operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an ASIC. A hardware component may also comprise programmable logic or circuitry temporarily configured by software to perform specific operations. For example, a hardware component may comprise software executed by a general-purpose processor or another programmable processor. Once configured by such software, the hardware component is no longer a general-purpose processor, as it becomes a specific machine (or a specific component of a machine) uniquely tailored to perform the function for which it was configured. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. Thus, the phrase "hardware component" (or "hardware-implemented component") should be understood to encompass a tangible entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a particular manner or to perform particular operations described herein. Considering embodiments in which the hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance. For example, if the hardware components consist of a general-purpose processor that is configured by software to be a special-purpose processor, the general-purpose processor may be configured at different times as different special-purpose processors (e.g., with different hardware components).The software may accordingly configure, for example, a particular processor or group of processors to be particular hardware components at one instance of time, while being different hardware components at a different instance of time.

[0113] Hardware components can provide information to and receive information from other hardware components. Thus, the described hardware components can be considered to be communicatively coupled. When multiple hardware components exist contemporaneously, communication can be achieved by signal transmission between or among two or more of the hardware components (e.g., via appropriate circuits and buses). In embodiments in which multiple hardware components are configured or instantiated at different times, communication between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures accessed by the multiple hardware components. For example, a hardware component can not only perform an operation but also store the output of that operation in a communicatively coupled memory device. Another hardware component can then retrieve and process the stored output at a later date by accessing the memory device.

[0114] A hardware component may initiate communication with an input or output device or operate on a resource (e.g., a collection of information). Various operations of the example methods described herein may be performed, at least in part, by one or more processors that are temporarily (e.g., by software) or permanently configured to perform the associated operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein. As used herein, a "processor-implemented component" refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least in part processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented components. Additionally, one or more processors may operate to support execution of the associated operations in a "cloud computing" environment or as "software as a service" (SaaS). For example, at least some of the operations may be performed by a group of computers (as an example of a machine including multiple processors), and these operations may be accessible over a network (e.g., the Internet) and through one or more appropriate interfaces (e.g., APIs). Certain performance of the operations may reside not only within a single machine, but also be distributed among processors deployed across multiple machines. In some exemplary embodiments, the processor or processor-implemented components may be located in a single geographic location (e.g., in a home environment, an office environment, or a server farm). In other exemplary embodiments, the processor or processor-implemented components may be distributed across multiple geographic locations.

[0115] "Processor" in this context means any circuit or virtual circuit (a physical circuit emulated by logic running on an actual processor) that manipulates data values ​​in accordance with control signals (e.g., "commands," "opcodes," "machine code," etc.) as well as generating corresponding output signals that are applied to operate a machine. A processor may be, for example, a CPU, a RISC processor, a CISC processor, a GPU, a DSP, an ASIC, an RFIC, or any combination thereof. A processor may also be a multi-core processor having two or more independent processors (sometimes called "cores") capable of simultaneously executing instructions.

[0116] A "timestamp" in this context refers to a string of characters or coded information that identifies when a particular event occurred, providing, for example, a date and time, sometimes with an accuracy of a fraction of a second.

[0117] Changes and modifications can be made to the disclosed embodiments without departing from the scope of the disclosure. These and other changes or modifications are intended to be included within the scope of the disclosure, as set forth in the following claims.

[0118] The Abstract of the Disclosure is provided to enable the reader to quickly identify the characteristics of the technical disclosure. It should be understood that it is not used to interpret or limit the scope or meaning of the claims. Additionally, in the foregoing Detailed Description, various features may be grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may lie in fewer than all features of a single disclosed embodiment. Accordingly, the appended claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.

Claims

1. 1. A method comprising: one or more processors receiving a plurality of observed speed points, wherein each of the plurality of observed speed points corresponds to a different segment of a plurality of segments of the observed trajectory; processing the plurality of observed speed points corresponding to the observed trajectory by a machine learning technique to generate a plurality of predicted speed points, independent of a sampling rate of the plurality of observed speed points, wherein the machine learning technique is trained to establish a relationship between a plurality of training observed speed points and a plurality of training predicted speed points; accessing individual sampling rates associated with the plurality of observed speed points after generating the plurality of predicted speed points independent of the sampling rates of the plurality of observed speed points; determining a future trajectory based on the plurality of predicted speed points and the respective sampling rates; determining that a target access control device is within a threshold range of the future trajectory; responsive to determining that the target access control device is within the threshold range of the future trajectory, performing processing associated with the target access control device.

2. the plurality of observed velocity points includes a plurality of acceleration measurements; the plurality of predicted speed points includes a plurality of acceleration measurements; the target access control device includes a lock associated with a door; The method of claim 1 , wherein performing the action includes unlocking the door.

3. establishing a wireless communication link between a user's mobile device and the target access control device; exchanging authentication information over said wireless communication link; The method of claim 2 , further comprising: performing the action after determining that the user is authorized to access the target access control device based on the authentication information.

4. determining, based on the authentication information, that the user is authorized to access the target access control device before performing the operation; 4. The method of claim 3, further comprising delaying performing the action after determining that the user is authorized until the target access control device is determined to be within the threshold range of the future trajectory.

5. the wireless communication link includes a Bluetooth Low Energy (BLE) communication protocol; The method of claim 3 or 4, wherein the plurality of observed velocity points are received via an Ultra Wideband (UWB) communication protocol.

6. The method of claim 5 , wherein the target access control device is located indoors.

7. determining, based on the authentication information, that the user is authorized to access the target access control device before performing the operation; 7. The method of claim 3, further comprising: in response to determining that the target access control device is outside the threshold range of the future trajectory, preventing the operation from being performed after determining that the user is authorized.

8. The method of any one of claims 1 to 7, wherein the machine learning technique comprises a neural network.

9. receiving the plurality of observed velocity points receiving a first data point indicating a first two-dimensional (2D) or three-dimensional (3D) Cartesian coordinate at a first time point; receiving second data points indicative of second 2D or 3D Cartesian coordinates at a second time point, wherein each of the first and second data points corresponds to a first interval of the plurality of intervals; 9. The method of claim 1, comprising calculating a first observed speed point of the plurality of observed speed points as a function of a difference between the first 2D or 3D Cartesian coordinate and the second 2D or 3D Cartesian coordinate and a difference between the first time point and the second time point.

10. Determining the future trajectory based on the plurality of predicted speed points includes: determining a sampling rate at which the plurality of observed speed points are received based on a difference between a first timestamp of a first observed speed point of the plurality of observed speed points and a second timestamp of a second observed speed point of the plurality of observed speed points; 10. The method of claim 1, further comprising: calculating three-dimensional Cartesian coordinates of the future trajectory based on the plurality of predicted speed points, a user's current position, and the sampling rate at which the plurality of observed speed points are received.

11. The machine learning technique, obtaining a first set of training data including a first set of the plurality of training observed speed points and a corresponding first set of the training predicted speed points; processing the first set of training observed speed points using the machine learning technique to generate a plurality of estimated speed points; calculating a loss based on the deviation between the plurality of estimated speed points and the corresponding first set of training predicted speed points based on a loss function; and The method of any one of claims 1 to 10, further comprising training by updating a plurality of parameters of the machine learning technique based on the calculated loss function.

12. the first set of training observation speed points corresponds to a first sampling rate; acquiring a second group of training data including a second set of the plurality of training observation rate points, the second set of the plurality of training observation rate points corresponding to a second sampling rate; processing a second set of the plurality of training observed speed points using the machine learning technique to generate a second plurality of estimated speed points; calculating a second loss based on the deviation between the second plurality of estimated speed points and the corresponding first set of training predicted speed points based on the loss function; The method of claim 11 , further comprising updating a plurality of parameters of the machine learning technique based on the second loss.

13. 1. A system comprising: one or more processors coupled to a memory containing non-transitory computer instructions, the non-transitory computer instructions, when executed by the one or more processors, cause a plurality of operations to be performed; The plurality of processes include: receiving a plurality of observed speed points, each of the plurality of observed speed points corresponding to a different one of a plurality of sections of the observed trajectory; processing the plurality of observed speed points corresponding to the observed trajectory by a machine learning technique to generate a plurality of predicted speed points, independent of a sampling rate of the plurality of observed speed points, wherein the machine learning technique is trained to establish a relationship between a plurality of training observed speed points and a plurality of training predicted speed points; accessing individual sampling rates associated with the plurality of observed speed points after generating the plurality of predicted speed points independent of the sampling rates of the plurality of observed speed points; determining a future trajectory based on the plurality of predicted speed points and the respective sampling rates; determining that a target access control device is within a threshold range of the future trajectory; responsive to determining that the target access control device is within the threshold range of the future trajectory, performing an action associated with the target access control device.

14. the plurality of observed velocity points includes a plurality of acceleration measurements; the plurality of predicted speed points includes a plurality of acceleration measurements; the target access control device includes a lock associated with a door; The system of claim 13 , wherein performing the plurality of actions includes unlocking the door.

15. The plurality of processes include: establishing a wireless communication link between a user's mobile device and the target access control device; exchanging authentication information over said wireless communication link; The system of claim 14 , further comprising: performing the action after determining that the user is authorized to access the target access control device based on the authentication information.

16. The plurality of processes include: determining, based on the authentication information, that the user is authorized to access the target access control device before performing the operation; 16. The system of claim 15, further comprising: delaying performing the action after determining that the user is authorized until the target access control device is determined to be within the threshold range of the future trajectory.

17. 1. A non-transitory computer-readable medium comprising non-transitory computer-readable instructions for performing a plurality of processes, the plurality of processes comprising: receiving a plurality of observed speed points, each of the plurality of observed speed points corresponding to a different one of a plurality of sections of the observed trajectory; processing the plurality of observed speed points corresponding to the observed trajectory by a machine learning technique to generate a plurality of predicted speed points, independent of a sampling rate of the plurality of observed speed points, wherein the machine learning technique is trained to establish a relationship between a plurality of training observed speed points and a plurality of training predicted speed points; accessing individual sampling rates associated with the plurality of observed speed points after generating the plurality of predicted speed points independent of the sampling rates of the plurality of observed speed points; determining a future trajectory based on the plurality of predicted speed points and the respective sampling rates; determining that a target access control device is within a threshold range of the future trajectory; performing processing associated with the target access control device in response to determining that the target access control device is within the threshold range of the future trajectory.

18. the plurality of observed velocity points includes a plurality of acceleration measurements; the plurality of predicted speed points includes a plurality of acceleration measurements; the target access control device includes a lock associated with a door; 20. The non-transitory computer-readable medium of claim 17, wherein performing the plurality of operations includes unlocking the door.

19. The plurality of processes include: establishing a wireless communication link between a user's mobile device and the target access control device; exchanging authentication information over said wireless communication link; 20. The non-transitory computer-readable medium of claim 18, further comprising: performing the processing after determining that the user is authorized to access the target access control device based on the authentication information.

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