Abstract: Example embodiments described herein relate to a camera configuration system to perform operations that include: capturing an image at a sensor device, the image comprising a set of image features; accessing a calibration template in response to the capturing the image at the sensor device; generating a presentation of the image, the presentation of the image including a display of the calibration template overlaid upon the image; and causing display of the presentation of the image at a client device.

Abstract: Controller devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload received data and analyses to the remote management server via wired or wireless communications. The controller devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the controller device, provide real-time information from the controller device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Abstract: Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.

Abstract: A video gateway device at a worksite (or other location) automatically locates cameras on the network, authenticates the gateway device with the cameras, and initiates streaming of a video stream (and/or other camera sensor data) from the cameras. For example, a worksite with existing cameras from multiple manufacturers, models, and/or capabilities may all be automatically registered with the video gateway devices through a series of automated communication and authentication attempts.

Abstract: Example embodiments described herein therefore relate to an object-model based event detection system that comprises a plurality of sensor devices, to perform operations that include: generating sensor data at the plurality of sensor devices; accessing the sensor data generated by the plurality of sensor devices; detecting an event, or precursor to an event, based on the sensor data, wherein the detected event corresponds to an event category; accessing an object model associated with the event type in response to detecting the event, wherein the object model defines a procedure to be applied by the event detection system to the sensor data; and streaming at least a portion of a plurality of data streams generated by the plurality of sensor devices to a server system based on the procedure, wherein the server system may perform further analysis or visualization based on the portion of the plurality of data streams.

Abstract: An analytics reporting system to perform operations that include: aggregating sensor data collected from a plurality of sensor devices within a database, the sensor data comprising a set of values that correspond with a metric; generating a threshold value based on the set of values that correspond with the metric; accessing a portion of the sensor data based on an identifier associated with the portion of the sensor data; determining the portion of the sensor data transgresses the threshold value; and generating a report that comprises a display of the portion of the sensor data based on the determining that the portion of the sensor data transgresses the threshold value.

Abstract: A system receives historical vehicle battery data from a gateway device connected to a vehicle. Some vehicles with plug-in rechargeable batteries recommend/require that the vehicle computer be turned off when recharging. Thus, obtaining a current state of charge while a vehicle is charging can be difficult because the vehicle computer can be off. While the vehicle/gateway device is unable to transmit current battery data, the systems estimate a battery charge from the historical data.

Abstract: A method comprising receiving first data from a first gateway device, determining that the first data includes an identifier of a first vehicle, and responsive to determining that the first data includes the identifier of the first vehicle, determining, based on the identifier of the first vehicle, that the first vehicle is a known vehicle in a fleet management system and responsive to determining, based on the identifier of the first vehicle, that the first vehicle is a known vehicle in the fleet management system, coupling the first gateway device with the first vehicle in the fleet management system, wherein the coupling causes data received from the first gateway device to be identified as data of the first vehicle.

Abstract: A method and system for monitoring a non-rechargeable battery of an IoT device are described. Voltage values of the non-rechargeable battery when a load is applied to the non-rechargeable battery are determined over a first interval of time. Based at least in part on these voltage values a determination of whether a voltage of the non-rechargeable battery has decreased for more than a predetermined threshold is performed. In response to determining based at least in part on the voltage values that the voltage of the non-rechargeable battery has decreased for more than the predetermined threshold, a prediction of an end of life parameter value for the non-rechargeable battery is performed, and the end of life parameter value is transmitted to an electronic device of an administrator of the IoT device.

Abstract: Methods for performing operations for improving driver safety across a fleet of vehicles are disclosed. A plurality of safety events pertaining to a driving of a fleet of vehicles by a plurality of drivers are detected. A subset of the events is identified. The subset corresponds to one or more safety events of the plurality of safety events involving one or more vehicles of the fleet of vehicles to which drivers have not been assigned. A user interface is generated for presentation on a client device, the user interface including an interactive user interface element for accessing the subset of the events. One or more user interface elements are provided for accepting or rejecting recommendations for assignments of one of the plurality of drivers to each of the vehicles. The recommendations are generated based on an application of a machine-learned model to images of faces captured.

Abstract: A system for providing remote control of assets is disclosed herein. The system provides secure communications with one or more assets and receive operational data from the one or more assets. The system provides a graphical interface that includes one or more icons, one or more indicators, and one or more user input interfaces. The system receives a user input via the one or more user input interfaces and transmit the user input to an asset associated with the one or more user input interfaces. The system detects changes to an operational characteristics of the asset and display the changes.

Abstract: A system for providing remote monitoring of assets is disclosed. The system provides secure communication with one or more assets and receive operational data from the one or more assets. The system generates a graphical user interface that be used for selection of inputs from the one or more assets and specification of conditions to be applied to inputs for generation of alerts. The system can receive a selection of one or more asset outputs and two or more conditions. The conditions are applied to the selection of one or more assets to generate alerts when at least one of the conditions is satisfied.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Abstract: A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and/or location data. The system receives data from multiple vehicles. The vehicle gateway device gathers vehicle metric data and correlates the metric data with location data. The system presents the correlated data in an interactive map graphical user interface.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for customized vehicle operator workflows. A vehicle operator workflow includes a set of tasks to be completed by a vehicle operator. The vehicle operator workflow may be presented to the vehicle operator upon satisfaction of a triggering event, such as upon the vehicle operator logging into an account or the vehicle entering a specified geographic region. The vehicle operator workflow may enable the vehicle operator to complete the individual tasks as well as submit evidence that the tasks have been completed.

Abstract: An Internet of Things (IoT) or vehicle dash cam may store both a high-resolution and low-resolution video stream on a device. The video streams are selectively accessible by remote devices. Because of the relatively smaller storage requirements of low-resolution video files, retaining of additional video data on the vehicle device (beyond what would be possible with only high-resolution video) is possible. The user may be provided an option to adjust the amount of low-resolution and high-resolution video to store on the device. A combined media file may be generated by a device to include time-synced high-resolution video, low-resolution video, and/or metadata for a particular time period.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Abstract: A gateway device for providing a dynamic network activation and/or handover is disclosed herein. The gateway device may activate on a first network associated with a primary network subscription identifier and determine to switch to a secondary network associated with a secondary network subscription identifier. The gateway device may disconnect from the first network and activate on the secondary network. The gateway device may access a cached list of networks for the primary network subscription identifier and scan for available networks while active on the secondary network. Based at least in part on the scan, the gateway device may determine that a second network of the cached list of networks is available. In response, the gateway device can disconnect from the secondary network and connect to the second network.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Abstract: Controller devices may be configured to execute a network discovery service to identify other devices on a network, including other controller devices, user computing devices, and/or human machine interface devices. The controller devices may communicate with the devices on the network. An individual controller device may, upon connection to a human machine interface device, provide to the human machine interface device via a web server, a graphical user interface from which a user may configure the controller device or connect to another controller device on the network.