Abstract: An emergency lighting system that includes a storage device, an emergency lighting device, an area control device and an external device. The emergency lighting device includes a light, a first controller having a first electronic processor configured to perform a self-diagnostic test, and a first input/output (I/O) device configured to transmit data associated with the self-diagnostic test. The area control device includes a second I/O device configured to receive data associated with the first self-diagnostic test form the first I/O device and transmit the data associated with the self-diagnostic test to the storage device.

Abstract: Embodiments of the present disclosure relate to a vehicle user interface. The vehicle user interface may receive user input from an input system. It may present user selectable options or prompt user action via an output system. The vehicle user interface may transmit, via a communication interface, to a computing system a series of user inputs received from at least the first input system, wherein the computing system is configured to extract at least one feature from the series of user inputs and generate a prediction model based on the at least one feature. At least one predicted option may be identified based on the prediction model. The vehicle user interface may instruct the first output system to present the at least one predicted option.

Abstract: Executable code is part of an over-the-air (OTA) update received by, for example, a computing device in a vehicle. In one example, the update is a secure over-the-air (SOTA) update of software that is stored in firmware of a vehicle component (e.g., firmware stored in memory of a storage device or a boot device that are mounted in a vehicle).

Abstract: Embodiments are directed to system and methods for determining the user identity of a vehicle occupant and identifying a corresponding user profile to provide customer settings for the user. In some embodiments, the seat or position of the user is determined and settings are applied depending on the which seat is occupied by the user. In some embodiments, vehicle settings are updated as additional vehicle occupants enter the vehicle.

Abstract: A driver recognition system of a vehicle identifies a person approaching the vehicle as a driver or other user. In one approach, the driver recognition system collects data using one or more cameras of the vehicle. The collected data corresponds to the person approaching the vehicle. Based on the collected data, a computing device determines (e.g., using a machine-learning model) whether the person is a user (e.g., driver) associated with the vehicle. If the person is a user associated with the vehicle, then the computing device causes one or more actions to be performed for the vehicle (e.g., controller configuration, boot up of a computing device, updating software using over-the-air update, etc.).

Abstract: A vehicle or a mobile device within or near the vehicle can have multiple sensors to authenticate a passenger or driver of the vehicle in different ways, e.g., fingerprint, facial recognition, voice fingerprinting, iris scan, etc. Also, non-biometric factors can be used to authenticate the passenger or driver of the vehicle, e.g., MAC address, RFID, username and password, PIN, etc. In addition, a network attached security asset accessed by a vehicle can be included within the vehicle such as modem within the vehicle with authentication capabilities. Also, the authentication can be according to a zero trust framework or networking methodology. Some or all of such credentials and authentication factors or methods can fail individually, at least in part, in various conditions. An intelligent system, making use of intelligent multi-factor authentication, can combine such information to determine the identity of the passenger or driver with more reliability.

Abstract: A controller of a vehicle detects persons that are approaching the vehicle and uses sensors to collect data regarding the persons to determine whether a security risk exists. In one approach, the collected data is used to determine whether each person is associated with the vehicle. In response to determining that at least one of the persons is unknown, a security risk is evaluated based on data from monitoring of the environment of the vehicle. In response to determining that a security risk score exceeds a threshold, one or more actions for the vehicle are performed to make the vehicle more secure.

Abstract: A security event identification system may enable obtaining, for each of the set of web requests, a screenshot of a corresponding web path resulting from the web request; applying a hash to each obtained screenshot; and determining, based on a comparison of the hashed screenshots, whether a security event exists related to the set of web requests.

Abstract: A vehicle or a mobile device within or near the vehicle can have multiple sensors to sense biometric features of a user or driver, and electronic circuitry (such as a computing system) can classify the user or driver according to the sensed biometric features. Also, non-biometric factors of the user or driver or of the mobile device of the user or driver can be used to classify the user or driver, e.g., MAC address, RFID, username and password, PIN, etc. Also, factors from interaction with a user interface of the vehicle or the mobile device can be used to classify the user or driver. Such features and factors can be used alone or in combination for the classification, and the classification can use AI (such as an ANN). The vehicle or the mobile device can then selectively enable or disable features of the vehicle based on the classification.

Abstract: Various embodiments of the present disclosure are directed to virtual vehicle interface that allows a user to adjust a vehicle's drive settings to make the driving experience more customized. For example, a user may select a desired response time, sensitivity, handling, or other criteria through a user interface of a mobile device. When the mobile device is connected to the vehicle, the vehicle adopts the user's specified settings to provide a customized driving experience.

Abstract: A profile is automatically generated for an occupant of a vehicle. In one approach, data is collected from an interior of a vehicle to determine whether an occupant is present. If an occupant is present, a local profile is automatically generated. The local profile is sent to a remote computing device. The remote computing device links the local profile to a remote profile stored by the remote computing device. Configuration data is generated by the remote computing device based on linking the local and remote profiles. The configuration data is sent to the vehicle and used by the vehicle to control the operation of one or more components of the vehicle.

Abstract: A system in a vehicle can have a camera and/or a sensor. Such a camera can be configured to record a visual feature of a user in the vehicle and send data derived from the visual feature. Such a sensor can be configured to sense a non-visual feature of the user in the vehicle and send data derived from the non-visual feature. The system can have a computing system configured to receive such data from the camera and the sensor. The computing device can also be configured to determine a state of the user based on the received data derived from visual and non-visual features, as well as an AI system. The computing device can also be configured to enable or disable a function of the vehicle based at least partially on the state of the user, which the AI system can infer from the received data.

Abstract: A system that has a plurality of controllers of a vehicle configured to execute functions of the vehicle as well as a gateway of the vehicle configured to secure communications between the plurality of controllers and resources of the controllers. To secure the communication, the gateway can be configured to generate links between the controllers and host devices having the resources according to a stored group of approved devices as well as establish, via the links, secured channels for communication between the controllers and the host devices. The system can also include a caching buffer, configured to hold data to be stored to and retrieved from data storage nodes of a distributed data storage system of the vehicle. The distributed data storage system can be configured to store data used by operations performed by the controllers and the gateway.

Abstract: Embodiments of the present disclosure relate to a vehicle user interface. The vehicle user interface may receive user input from an input system. It may present user selectable options or prompt user action via an output system. The vehicle user interface may transmit, via a communication interface, to a computing system a series of user inputs received from at least the first input system, wherein the computing system is configured to extract at least one feature from the series of user inputs and generate a prediction model based on the at least one feature. At least one predicted option may be identified based on the prediction model. The vehicle user interface may instruct the first output system to present the at least one predicted option.

Abstract: Embodiments of the present disclosure relate to assisted driving of a vehicle. The response time of the driver may be measured and control operations may be adjusted to accommodate or compensate the response time of the driver. Predictive actions in anticipation of a slightly delayed input from the driver may be taken to avoid potentially dangerous conditions. A vehicle may be controlled to slow down to allow more time to a potential event to accommodate a slow response from the driver.

Abstract: Methods, systems, and media for presenting a reminder to view content are provided. In accordance with some embodiments, a method for presenting a reminder to view content is provided, the method comprising: determining that a message that includes a link to a first content item has been received by a user device; determining whether the link to the content item has been selected; in response to determining that the link to the content item has not been selected, storing metadata associated with the content item; determining that a user is viewing a second content item that is related to the first content item; determining a probability value that indicates a likelihood that the user would be interested in viewing the first content item; and causing an interface that includes a reminder to view the first content item to be presented on the user device.

Abstract: A vehicle having one or more cameras, configured to record one or more images of a driver of the vehicle. The camera(s) can be configured to send biometric image data derived from the image(s). The vehicle can include a computing system configured to receive the biometric data and to determine a risk score of the driver based on the received biometric data and an AI technique, such as an ANN or a decision tree. The received biometric data or a derivative thereof can be input for the AI technique. The computing system can also be configured to transmit the risk score of the driver to the customer so that the customer can decide whether to book the vehicle for a ride.

Abstract: Systems, methods and apparatuses to detect an item left in a vehicle and to generate an alert about the item. For example, a camera configured in a vehicle can be used to monitor an item associated with a user of the vehicle. The item as in an image from the camera can be identified and recognized using an artificial neural network. In response to a determination that the item recognized in the image is left in the vehicle after the user has exited the vehicle, an alert is generated to indicate that an item is in the vehicle but the user is leaving the vehicle.

Abstract: A system for ANN training through processing power of parked vehicles. The system can include a master computing device having a controller configured to control training of an ANN. The training can be performed at least partially in separate parts by computing devices of parked vehicles. The controller can be configured to separate computing tasks of training the ANN into separated tasks. Also, the controller can be configured to assign at least some of the separated tasks to selected computing devices of parked vehicles. The controller can also be configured to receive and assemble results of the separated tasks to train the ANN. The controller can also be configured to train the ANN according to the results. The master computing device can be configured to send the assigned tasks to the selected devices of the vehicles as well as receive, from the selected devices, the results of the assigned tasks.

Abstract: A vehicle having one or more cameras, configured to record one or more images of a person approaching the vehicle. The camera(s) can be configured to send biometric data derived from the image(s). The vehicle can include a computing system configured to receive the biometric data and to determine a risk score of the person based on the received biometric data and an AI technique, such as an ANN or a decision tree. The received biometric data or a derivative thereof can be input for the AI technique. The computing system can also be configured to determine whether to notify a driver of the vehicle of the risk score based on the risk score exceeding a risk threshold. The vehicle can also include a user interface, configured to output the risk score to notify the driver when the computing system determines the risk score exceeds the risk threshold.