Abstract: Systems and methods are disclosed for operating an autonomous vehicle based on real-time operating data. The operating data may be data about vehicles, drivers, passengers, as well as relevant environmental conditions and contextual data. In some cases, historical data for the preceding data types may be used. The systems and methods may obtain a set of real-time operating data indicative of one or more behaviors of an autonomous vehicle. One or more operations may be performed on the set of real-time operating data. An instruction to modify a particular vehicle operation may be generated based on output from the operations and the one or more behaviors of the autonomous vehicle, and the instruction to modify the particular vehicle operation may be provided to a particular processor that is on-board the vehicle and that controls the particular vehicle operation, to thereby automatically modify the particular vehicle operation.

Abstract: Systems and methods are provided for dynamically protecting transportable articles in vehicles. A system for dynamically protecting a transportable article in a vehicle may include one or more processors and non-volatile memory storing instructions. The instructions, when executed by the one or more processors, cause the system to determine at least one of a characteristic or a trait of the transportable article; detect, based on sensed data, an emergency condition; select one or more article protection components based on (i) the at least one of the characteristic or the trait of the transportable article, and (ii) the detected emergency condition; and in response to detecting the emergency condition, deploy the selected one or more article protection components to protect the transportable article.

Abstract: A computer-implemented method includes receiving software version data generated by an on-board system of a vehicle via a network, wherein the software version data indicates a version of software executed by the on-board system to control operational parameters of the vehicle. The method also includes determining, based at least upon (i) the software version data and (ii) a known current version of the software, whether a latest software update was installed for the on-board system, setting, based at least upon whether the latest software update was installed for the on-board system, at least a portion of a driver profile associated with the driver, and transmitting the at least the portion of the driver profile to a third party server.

Abstract: In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Abstract: Systems and methods are described for generating a blockchain-based user profile. In various aspects, one or more blockchain IDs associated with a user is received, where each blockchain ID is associated with a corresponding blockchain. One or more blockchain transactions are identified that are associated with the one or more blockchain IDs, where a trust profile for the user can be generated based on the one or more blockchain transactions. The trust profile can include user information determined from the one or more blockchain transactions.

Abstract: Disclosed systems and methods incorporate machine learning to assess damage to vehicles. An example method includes: accessing image data representing one or more digital images of damage to a vehicle; selecting, using one or more processors, an applicable machine learning algorithm from a plurality of different trained machine learning algorithms, wherein the plurality of different machine learning algorithms are trained for respective different combinations of one or more of vehicle make, vehicle model, vehicle year, or area of damage; processing the image data, with the applicable machine learning algorithm using one or more processors, to determine assessed damage for the vehicle; accessing actual damage information for the vehicle; determining, using one or more processors, differences between the actual damage and the assessed damage; and iterating, using one or more processors, the applicable machine learning algorithm based on the differences to improve its damage assessment accuracy.

Abstract: In a general aspect, signals are converted between regimes in a quantum computing system. In some cases, a quantum computing system includes: a quantum processing unit, a control system, a transmission medium, and circuitry. The quantum processing unit includes a superconducting circuit, which includes a plurality of qubit devices. The control system includes a signal generator configured to generate a first control signal and encode qubit control information in the first control signal. The transmission medium is configured to couple the signal generator with a signal conversion system. The signal conversion system is configured to: receive the first control signal generated by the signal generator; and generate a second control signal based on the qubit control information encoded in the first control signal. The circuitry is configured to deliver the second control signal to the plurality of qubit devices.

Abstract: Methods and systems for recommending safe vehicle seats and/or predicting the replacement time of vehicle seats. The systems and methods may include (1) training a machine learning model for predicting a replacement time of one or more vehicle seats using (i) a set of characteristics of a previously recommended vehicle seat and/or (ii) replacement times for the previously recommended vehicle seat; (2) receiving input data related to a previously recommended vehicle seat; (3) determining a set of characteristics of the input data; (4) applying the set of characteristics of the input data to the machine learning model to determine the predictive replacement time for replacing the one or more vehicle seats; and/or (5) providing an indication of the predictive replacement time for display on a client device.

Abstract: Methods and systems for recommending safe vehicle seats and/or predicting the replacement time of vehicle seats. The systems and methods may include (1) training a machine learning model using a set of characteristics of previously recommended vehicle seats to generate a vehicle seat recommendation score for one or more vehicle seats; (2) receiving a request for a vehicle seat recommendation; (3) receiving input data; (4) determining a set of characteristics of the input data; (5) applying the set of characteristics of the input data to the machine learning model to generate a vehicle seat recommendation score for the one or more vehicle seats; (6) ranking the one or more vehicle seats by vehicle seat recommendation score to generate a vehicle seat recommendation list; and/or (7) presenting the vehicle seat recommendation list to a client device.

Abstract: Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, an identity of a vehicle operator may be determined and a vehicle operator profile and/or operating data regarding autonomous operation features of the vehicle may be received after the vehicle operator opts into a rewards program and agrees to share their data. Autonomous operation and vehicle operator risk levels associated with operation of the autonomous or semi-autonomous vehicle may be determined. Based upon the risk levels and/or comparison thereof, one or more autonomous operation features may be disengaged. A preparedness level of the vehicle operator to assume or reassume control of operating the vehicle is determined prior to disengagement. If satisfactory, an alert is presented to the vehicle operator prior to disengagement of the autonomous operation features.

Abstract: In a computer-implemented method of detecting or predicting water damage within a structure, sensor data indicative of one or more conditions associated with the structure is generated. The condition(s) include at least one characteristic of air, construction materials, or water within the structure. The method may also include receiving the sensor data and data indicative of one or more other factors associated with an interior of the structure, detecting or predicting water damage to the structure by jointly analyzing the sensor data and the data indicative of the one or more other factors, and causing an indication of the detected or predicted water damage to be presented to a user.

Abstract: A system for detecting water damage to a structure may include a plurality of energy-harvesting sensors on or within the structure, each sensor being configured to (i) generate sensor data indicative of one or more characteristics of air, construction materials, and/or water within the structure and (ii) generate power for operating the sensor responsively to an external stimulus other than an electrical power source. One or more processors of the system receive sensor data generated by the energy-harvesting sensors, detecting water damage to the structure by analyzing at least the sensor data, and cause an indication of the detected water damage to be presented to a user.

Abstract: In a computer-implemented method of detecting or predicting water intrusion into a structure from an external environment, data indicative of one or more factors associated with the external environment is received. The method also includes detecting or predicting water intrusion into the structure by analyzing at least the data indicative of the one or more factors associated with the external environment, and causing an indication of the detected or predicted water intrusion to be presented to a user.

Abstract: A computer-implemented method of monitoring water usage includes generating and receiving first water usage data indicating a first plurality of water usage events associated with a structure, each of the first plurality of water usage events being associated with a respective location and/or entity. The method may also include generating, based upon the first water usage data, a baseline water usage model representing water usage over time locations and/or entities, and generating second water usage data indicating a second plurality of water usage events associated with the structure, each of the second plurality of water usage events being associated with a respective location and/or entity. The method may also include detecting, based upon the second water usage data and the baseline water usage model, anomalous water usage associated with a particular location and/or entity, and causing an indication of the anomalous water usage to be presented to a user.

Abstract: Vehicles may have autonomous capabilities that permit the vehicle to navigate according to varying levels of participation by a human operator. A human operator may be associated with a driving capability profile and the environment surrounding a vehicle may be associated with an environmental risk profile. A vehicle, or another party in communication with a vehicle, may determine whether, based on the human operator's driving capability profile and the environmental risk profile, it is likely that the vehicle will be navigated more safely if there is a change to the vehicle's autonomous capabilities and a corresponding change to the human operator's driving responsibilities. If it is determined that a change in autonomous vehicle capability is likely to increase safe navigation of the vehicle, an insurer may offer an insurance premium cost reduction if the human operator agrees to the proposed change in autonomous capabilities.

Abstract: Systems and methods for providing real-time feedback to a driver of a vehicle based on real-time datasets is disclosed. The systems and methods include obtaining one or more sets of real-time data associated with a vehicle. Performing one or more operations on the set of real-time data, wherein the one or more operations are based on the set of real-time data. Generating, a feedback alert based on results of the one or more operations and one or more detected real-time behaviors of the vehicle. Providing the feedback alert for presentation at a user interface on-board the vehicle. The real-time data may include contextual data, environmental data, operating data, data for a plurality of drivers, or combinations thereof.

Abstract: According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated.

Abstract: In a computer-implemented method, alert responsiveness data indicating how responsive a driver is to one or more types of vehicle alerts is received. The method also includes generating or modifying a driver profile associated with the driver, the driver profile including alert responsiveness profile information based upon the alert responsiveness data, and identifying, based at least upon the generated or modified driver profile, a suggested vehicle type. Identifying the suggested vehicle type includes determining that the generated or modified driver profile meets a set of one or more matching criteria associated with the suggested vehicle type at least in part by determining that the alert responsiveness profile information meets one or more criteria indicative of reliability of the suggested vehicle type. The method also includes causing an indication of the suggested vehicle type to be displayed to a user.

Abstract: In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Abstract: Systems and methods are disclosed for enhancing and developing a damage scene virtual reality (VR) visualization. Annotated immersive multimedia image(s) may be received from a first user, where the annotated immersive multimedia image(s) can be associated with a damage scene. A VR visualization of the annotated immersive multimedia image(s) may be rendered using a VR device associated with a second user. The VR visualization may be used to determine a damage amount, where the damage amount is determined from one or more damaged items identifiable in the annotated immersive multimedia image(s).