624 results about "Telematics" patented technology

A computing device for generating on-demand products based upon real-time geolocation data is configured to receive and store at least one product definition associated with a respective product. Each product definition defines an availability of the product and includes a product availability location. The computing device is also configured to receive real-time location data from a user computing device, and periodically query a memory device with the real-time location data. The computing device detects that the user computing device has entered a first location by retrieving, in response to a successful query of the memory device, a first product definition associated with a first product having a product availability location including the first location. The computing device is also configured to generate a product identification message including at least a portion of the first product definition, and transmit the product identification message to the user computing device.

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.

The present disclosure relates to systems and methods for retrieving telematics data. A method for retrieving telematics data may comprise operating at least one processor to: provide at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database; receive a natural language request from a user; generate, using a large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database; execute the executable query for retrieving the portion of the telematics data from the at least one telematics database; and return at least the portion of the telematics data to the user, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database.

A system (100) for adaptive real-time risk assessment in insurance auditing, comprising the following: a data acquisition module configured to collect real-time data from multiple sources, including IoT-based health monitoring devices, telematics systems, financial transaction logs, and external risk databases; a risk analysis module configured to process and analyze the collected data using machine learning algorithms to create a dynamic risk assessment for a person or company; a policy recommendation module configured to generate personalized underwriting decisions and premium rates based on dynamic risk assessment; a fraud detection module configured to detect anomalies and potentially fraudulent claims using AI-based anomaly detection methods; an explainability and compliance module configured to ensure transparency in underwriting decisions by providing an interpretable AI framework that complies with regulatory standards; and a real-time feedback module configured to continuously update risk profiles and adjust underwriting parameters based on new data inputs.

A system includes a refuse vehicle, multiple refuse containers, and a remote computing system. The refuse vehicle includes a telematics unit. The refuse containers are positioned at different customer locations. Each of the refuse containers include a sensor configured to measure a fill level and a wireless transceiver. The remote computing system includes processing circuitry configured to obtain a status of the refuse vehicle from the telematics unit. The processing circuitry is also configured to obtain sensor data from the refuse containers indicating the fill level of each of the refuse containers. The processing circuitry is also configured to determine a route for the refuse vehicle based on the status of the refuse vehicle and the sensor data obtained from the refuse containers. The processing circuitry is also configured to transmit the route to the telematics unit of the refuse vehicle.

A computing system, for automated generation of instructional text for driver safety, executes steps including: 1) accessing predefined datasets of telematic alerts, of predefined alert sequences, and of sequence categories; 2) acquiring one or more alert vectors during driving, each alert associated with a time of occurrence; 3) scanning each of the one or more alert vectors, over multiple time windows, to identify a set of alert sequence occurrences matching alert sequences in the sequence dataset and conforming to predefined time windows; 4) according to the severity of each alert sequence in the identified set of alert sequences, increasing by a proportional amount a priority score of a corresponding sequence category, to generate an aggregate priority score for each sequence category; and 5) providing instructional text associated with said sequential category from a text dataset to the driver.

Embodiments described herein, inter alia, receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by a potential renter during the period of time; identify, upon analyzing the telematics data, driving behavior(s) of the renter during the period of time; determine, for each driving behavior, a corresponding state of an environment of the vehicle when the driving behavior occurred; determine renter eligibility value(s) for the renter based on the driving behavior(s) and the corresponding state of an environment of the vehicle; compare the renter eligibility value(s) to user preference value(s) of a profile of a vehicle owner, wherein the user preference value(s) define one or more criteria for vehicle renters with whom the vehicle can be shared; and cause an indication of the vehicle associated with the profile to be displayed only if the renter satisfies the criteria.

In some implementations, the techniques described herein relate to a method including: receiving a data log entry associated with a driver that includes a service provider location and a timestamp; identifying a vehicle associated with the driver based on the data log entry by identifying the vehicle includes applying a machine learning model to the data log entry and a vehicle database; loading a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry includes applying a rule-based optimization algorithm to a historical service provider database; and transmitting a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry.

The invention discloses a vehicle control method and device, a terminal and remote information processing equipment. The method may include: a terminal negotiating with a telematics device of a vehicle through a first link to determine a session key; encrypting the remote vehicle control instruction with the session key; the encrypted remote vehicle control instructions are sent to the telematics device over a second link, the second link being different from the first link. The first link is used when the terminal and the remote information processing equipment negotiate the session key, and then the encrypted remote vehicle control instruction is transmitted through the second link, that is, the key of the application is obtained through negotiation, the key cannot be obtained even if the cloud is attacked, and the link used for negotiating the key is different from the transmission link, so that the security of the terminal is improved. Even if the cloud is attacked under the condition that the instruction is transmitted through the cloud, an attacker cannot tamper the vehicle control instruction, so that the safety of the vehicle control method is ensured.

The invention discloses a vehicle fault file distributed uploading method and device, equipment and a medium. The method comprises the following steps: receiving a fault signal of a target vehicle through a remote information processing control unit, and extracting a part fault code and fault occurrence time according to the fault signal; collecting working condition data within a specified time range of fault occurrence time and generating a fault file; the method comprises the following steps: dividing a fault file into a plurality of sub-files according to the size of the fault file and the transmission capability of an SIM card, naming each sub-file according to a subpackage sequence, carrying out compression processing on each sub-file by adopting a compression algorithm, and sequentially uploading each compressed file to an Internet of Vehicles platform according to a sequence of file serial numbers in file names from small to large. According to the embodiment of the invention, each sub-file is named, compressed and uploaded by adopting a file subpackage strategy, so that the problem of large file transmission is solved, and the transmission efficiency is optimized; and special characters are added in the last sub-file, so that the integrity and accuracy of the fault file are ensured.

The present disclosure relates to a directed electric vehicle charging system for generating revenue and methods thereof. More particularly, this disclosure describes a telematics service provider that maintains relationships with charging stations and their associated stores such that revenue may be realized by directing vehicles to those charging stations. In an illustrative embodiment, the telematics service provider may receive a charge event from a vehicle and in turn, the provider provides an appropriate charging station to that vehicle. An amount spent at the charging station including its associated stores are tracked when the vehicle charges at the charging station. After completing the charging and the vehicle leaves the area, the amount spent is tallied and provided to the telematics service provider. A portion of the amount spent may then be sent to the telematics service provider for the recommended charging station.

A system for generating improved notifications relating to events is disclosed. The system includes a sensing network including an event sensor configured to sense events. The system further includes a sensed-event processing system configured to append meta data to information associated with a sensed event. The system further includes a human-machine interface and at least one communication unit configured to communicate amongst the human-machine interface, the sensing network, and the sensed event processing system, the information communicated from the sensing device via the at least one communication unit. The system further includes a notification generation system configured to generate a notification to a user of the human-machine interface, the notification content including at least one user-selectable action option. The communication unit configured to communicate the notification via the human-machine interface and select the notification content, the notification type, or the means of notification based on the meta data.

Systems and methods for detecting bad installations of telematics devices are provided. The method involves operating at least one processor to: receive image data associated with an installation of a telematics device in a vehicle; extract a portion of the image data containing the telematics device using a first machine learning model trained to detect the telematics device in the image data; determine whether the telematics device was correctly installed using a second machine learning model on the extracted portion of the image data, the second machine learning model trained to classify the telematics device in the extracted portion of the image data based on a position and orientation of at least one fastener attached to the telematics device; and automatically execute at least one action in response to and based on the determination of whether the telematics device was correctly installed.

A method for energy control can include receiving an operating point of an energy conversion device at a telematics interface of a data processing system, the telematics interface configured to receive information from the energy conversion device. The method can include generating, by the data processing system, a digital twin of the energy conversion device, the digital twin having a virtual operating point. The method can include predicting, by the data processing system, a future load associated with the energy conversion device. The method can include modeling a predicted response to the future load based on the digital twin, the predicted response corresponding to the virtual operating point. The method can include conveying, to the energy conversion device via the telematics interface, an adjustment to the operating point of the energy conversion device based on the predicted response to the future load.

A method for energy control can include receiving an operating point of an energy conversion device at a telematics interface of a data processing system, the telematics interface configured to receive information from the energy conversion device. The method can include generating, by the data processing system, a digital twin of the energy conversion device, the digital twin having a virtual operating point. The method can include predicting, by the data processing system, a future load associated with the energy conversion device. The method can include modeling a predicted response to the future load based on the digital twin, the predicted response corresponding to the virtual operating point. The method can include conveying, to the energy conversion device via the telematics interface, an adjustment to the operating point of the energy conversion device based on the predicted response to the future load.

A computer-implemented method is disclosed for determining an insurance premium amount. The computer-implemented method includes a step of obtaining, from a remote server, telematics data associated with operation of a vehicle corresponding to a time period. The computer-implemented method includes a step of extracting, based on the telematics data, a driving feature dataset comprising at least one of: a driving behavior dataset, an environmental condition dataset, or a combination thereof. The computer-implemented method includes a step of calculating, based on execution of a trained ensemble model on the extracted driving feature dataset, a risk score indicative of a probability of occurrence of a collision of the vehicle. The computer-implemented method includes a step of determining, based on the calculated risk score, the insurance premium amount. The computer-implemented method includes a step of storing the determined insurance premium amount in a database associated with an insurance service platform.

A method of utilizing vehicle telematics to create a vehicle wagering network by connecting vehicles to mapping and traffic data. Connected vehicles transmitting real-time telemetric data form the basis for wagers.

Systems and methods for detecting vehicle collisions are provided. The methods involve operating at least one processor to: receive telematics data originating from a telematics device installed in a vehicle, the telematics data including acceleration data; detect a putative collision event based on the acceleration data exceeding a predetermined acceleration threshold; identify a portion of the acceleration data associated with the putative collision event, the portion of the acceleration data spanning from a time prior to the putative collision event to a time subsequent to the putative collision event; identify at least one impulse in the portion of the acceleration data based on a predetermined jerk threshold; use a trained classifier on the at least one impulse to determine that the putative collision event is a collision event; and in response to determining the collision event, trigger at least one action responsive to the collision event.

A method for automatically calibrating a telematics device in a vehicle includes obtaining, from a vehicle device of the vehicle, a vehicle identification number (VIN) and retrieving a set of vehicle data using the VIN. The method also includes making a first determination that a first portion of vehicle characteristic data is included in the set of vehicle data. Further, the method includes setting event detection parameters for the telematics device based on the first portion of vehicle characteristic data.

Disclosed herein are systems and methods for estimating driver efficiency. For example, one such method may comprise operating at least one processor to: receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; identify, using the telematics data, a trip completed by at least one vehicle of the plurality of vehicles; determine an estimated trip difficulty of each trip based on a plurality of trip metrics associated therewith that relate to vehicle fuel consumption; determine, using the telematics data, a plurality of driver behavior metrics for each trip, each driver behavior metric corresponding to an action performable by a driver of the at least one vehicle; and determine, for each trip, a driver efficiency score based at least in part on the estimated trip difficulty and the plurality of driver behavior metrics thereof.

A method may include retrieving, via a processor of a computing system, insurance information associated with a user and receiving one or more alerts regarding an event that will occur within a determined radius of one or more properties within a period of time. The computing system may then determine a list of actions for the user to perform on the property based on the event and send the list of actions to a computing device associated with the user. Based on whether the user completes the list of actions, a premium rate of the user may be adjusted.

Described herein are systems and methods for identifying a diagnostic trouble code (DTC) cycle for a vehicle. In an embodiment, one such method may comprise operating at least one processor to: receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; identify, using the telematics data, a DTC generated by one or more of the plurality of vehicles, each DTC having a DTC status associated therewith; determine, for each DTC, the ignition cycle during which the DTC was generated; identify, for each DTC, a DTC status cycle by merging consecutive ignition cycles during which the DTC is generated with a consistent DTC status associated therewith; and identify, for each DTC, a DTC cycle based at least in part on each DTC status cycle identified for the DTC.

Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets and a plurality of sensor data sets; for each vehicle operator of the plurality of vehicle operators: generating and continually updating an operator profile, one or more telematics inferences, a data profile; and listing and continually updating the data profile onto a telematics marketplace; receiving a plurality of conditional bids for a target operator profile associated with a target data profile, each conditional bid of the plurality of conditional bids including one or more conditional payments and one or more payment conditions; determining, for each conditional bid of the plurality of conditional bids, a predicted bid-generated profit or a predicted bid-generated revenue; determining a winning bid and an associated winning bidder; and transmitting the target operator profile to the winning bidder.

A method for use aboard a motor vehicle having a vehicle interior and a vehicle telematics unit (VTU) includes detecting, using an electronic control unit (ECU), an active voice call between the VTU and a remote call center, and processing sensor data via the ECU. The sensor data is indicative of an operator of the vehicle having exited the interior. In response to enabling conditions, the voice call is transferred to a personal mobile device, e.g., a smartphone, external to the interior. The enabling conditions may include receipt by the ECU of a call transfer approval signal. A motor vehicle includes a body and control system attached thereto. The control system includes the VTU, a sensor suite, and the ECU. A computer-readable storage medium includes instructions, the execution of which by a processor of the ECU causes the method to be performed.

Described are various embodiments of a vehicle guiding system and method. In some embodiments, the system comprises an AI-augmented vehicle-specific constraints database combining multiple data sources and used for generating optimized routes. The system may further include an intermediate layer of links or interface configured to connect with known and new turn-by-turn driver tools / applications and allow the AI-augmented constraints database to be used. The system may be coupled with a plurality of telematics systems monitoring the vehicle to provide an independent feedback loop which allows the system to react in real-time, and establish performance metrics and measurements for invoicing and reporting purposes. In some embodiments, the system may be coupled to one or more traffic management subsystems or devices.

A system and method may collect telematics and / or other data, and apply the data to insurance-based applications. From the data, an insurance provider may determine accurate vehicle usage information, including information regarding who is using a vehicle and under what conditions. An insurance provider may likewise determine risk levels or a risk profile for an insured driver (or other drivers), which may be used to adjust automobile or other insurance policies. The insurance provider may also use the data collected to adjust behavior based insurance using incentives, recommendations, or other means. For customers that option to the data collection program offered, the present embodiments present the opportunity to demonstrate a low or moderate risk lifestyle and the chance for insurance-related savings based upon that low or moderate risk.

Disclosed herein are systems and methods for identifying service centers. One of such methods may comprise operating at least one processor to: receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; determine for each of the plurality of vehicles, using the telematics data, when a vehicle maintenance event has occurred by identifying at least a binary vehicle status indicator change, time-logged telematics data that meets a predetermined condition, an indication that a vehicle diagnosis has occurred, or a combination thereof; determine, using the telematics data, a location of each of the plurality of vehicles at a time at which the vehicle maintenance event occurred; and identify one or more service centers by: applying to a plurality of maintenance event locations a clustering model, and classifying one or more clusters of maintenance event locations as a service center.

A system and method AI-enabled telematics and actuation for electronic entertainment, simulation, training, and remote operations systems. The system and method disclosed support neuro symbolic reasoning and generative AI enabled experience generation to allow a user or collection of users to experience a wide range of realistic scenarios where the user can pick and choose an experience that best fits their individual or collective preferences. Additionally, the system and method have wide applications to a variety of environments, including but not limited to, racing, sports, military training, vehicle and aircraft operation, and training simulations. The proposed system and method enable realistic, immersive video game, simulation, training, and remote operations environments which are applicable to a wide range of devices, platforms, and mediums for recreational, commercial, industrial, and security uses.

A system (10) for detecting a disconnection of a vehicle system module (VSM) (28), the system (10) comprising: a memory (38) configured to include a program and a processor (36) configured to execute the program, the program enabling the processor (36) to: Detecting a separation event; and in response to detecting the separation event, collecting VSM location data; characterized in that the disconnect event is a hard-wired connection between the VSM (28) and a communication bus (58) being disconnected; wherein the disconnection event is detected when the VSM (28) has ceased to receive periodic heartbeat signals from one or more other VSMs (28) via the communication bus (58) or after the VSM (28) has transmitted a beacon test configured to indicate whether the VSM (28) is still firmly connected to one or more other VSMs (28).

Methods by a telematics device and a telematics device for simplifying captured telematics data and providing near real-time data points to a telematics server. Simplifying the data uses an algorithm that prioritizes primary data while providing timely secondary data under various conditions. In some implementations, a data simplification method is applied to partially filled data captured buffers containing secondary data. In some implementations, partially filled data capture buffers are processed if data points of that data type have not been selected for sending or if the data capture buffers are filled over a particular threshold. In some implementations, data points having an estimate error greater than an estimate error threshold are selected for sending to the telematics server.