Abstract: Methods and systems for monitoring and analyzing vehicle seats, vehicle seat installation, and/or vehicle seat operation. The systems and methods may include (1) obtaining, by one or more processors, image data of a vehicle seat located within a vehicle, wherein the image data may include one or more connecting points of the vehicle seat to the vehicle; (2) inputting, by the one or more processors, the image data into a machine vision model that is trained: (a) using historical image data of vehicle seats within vehicles, (b) to learn a relationship between extracted features of the historical image data and a properness of an installation of a vehicle seat, and/or (c) to output a determination of a properness of an installation of a vehicle seat in response to detecting input image data; and/or (3) presenting, by the one or more processors, an indication of the output of the machine vision model.

Abstract: Methods and systems for visualizing proper fastening of vehicle seats. The method and systems may include: (1) receiving, by one or more processors, input data that may include one or more of vehicle data, vehicle seat data, and/or child data; (2) receiving, by the one or more processors, underlay layer data indicative of a field of view (FOV) associated with an AR viewer device; (3) generating, by the one or more processors, overlay layer data based upon the input data, the overlay layer data may include an indication of a proper fastening of the vehicle seat; (4) correlating, by the one or more processors, the overlay layer data with the underlay layer data; (5) creating, by the one or more processors, an AR display based upon the correlation; and/or (6) presenting, by the one or more processors to the AR viewer device, the AR display.

Abstract: Methods and systems for validating proper fastening of vehicle seats via an application executing on a mobile computing device. The method and systems may include: obtain input data via an input interface of the mobile computing device, the input data including one or more of vehicle data, vehicle seat data, or child data; obtain underlay layer data generated by an image sensor of the mobile computing device; generate overlay layer data based upon the input data and/or the underlay layer data, the overlay layer data including an indication associated with proper fastening of the vehicle seat; corelate the overlay layer data with the underlay layer data; the one or more processors to create an AR display based upon the correlation; and/or present the AR display via an AR interface of the mobile computing device.

Abstract: Methods and systems for monitoring and analyzing vehicle seats, vehicle seat installation, and/or vehicle seat operation. The systems and methods may include (1) obtaining, by one or more processors, image data of a vehicle seat located within a vehicle, wherein the image data may include one or more connecting points of the vehicle seat to the vehicle; (2) inputting, by the one or more processors, the image data into a machine vision model that is trained: (a) using historical image data of vehicle seats within vehicles, (b) to learn a relationship between extracted features of the historical image data and a properness of an installation of a vehicle seat, and/or (c) to output a determination of a properness of an installation of a vehicle seat in response to detecting input image data; and/or (3) presenting, by the one or more processors, an indication of the output of the machine vision model.

Abstract: Systems and methods for simulating a vehicle seat in a vehicle using virtual reality. The systems and methods may generate a virtual configuration comprising a virtual vehicle seat model associated with a physical vehicle seat, and a virtual vehicle model associated with a physical vehicle, to be presented via the display of a viewer device.

Abstract: Systems and methods for simulating a vehicle seat in a vehicle using augmented reality. The systems and methods overlay a virtual vehicle seat model associated with a physical vehicle seat onto a physical vehicle via a display of the viewer device to generate a virtual configuration of the virtual vehicle seat model in the physical vehicle.

Abstract: A system and method are provided for controlling an interior configuration of a vehicle following a collision. Sensor data that includes, or is derived from data that includes, data collected by one or more sensors is received, and a vehicle accident condition indicative of an accident having occurred is detected by processing the sensor data. After detecting the vehicle accident condition, an actuator component is caused to prevent a passenger from adjusting an interior vehicle component outside a predetermined range of physical configurations, while allowing the passenger to adjust the interior vehicle component within the predetermined range of physical configurations.

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: 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 he 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: Systems and methods for determining the performance of a driver of a vehicle based on changes, over time, in the context and environment in which the vehicle operates, and any resultant driver behavior are disclosed. A set of driver response data is created from based on an analysis of time-series data indicative of the driver's operation of the vehicle in conjunction with time-series data indicative of changes in the vehicle's context/environment. The driver response data indicates the types and magnitudes of the driver's responses to various changes in the vehicle's operating context/environment and the driver's time-to-respond for each of the responses. That is, the driver response data indicates how a driver compensated his or her behavior (if at all) in response to different changes in the vehicle's context and/or environment. The driver response data may be compared to one or more thresholds to determine the driver's performance.

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: 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 arm provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

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: 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: 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: 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: 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 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.