928 results about "Telematics" patented technology

The present disclosure relates to systems and methods for retrieving telematics data as well as for training machine learning models. A method for retrieving telematics data may comprise operating at least one processor to: provide a plurality of databases, each database storing at least telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; receive a natural language request from a user, the natural language request comprising at least one textual question relating to the telematics data stored within one of the plurality of databases; generate, using a large language model (LLM) that does not have access to the plurality of databases, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the database by inputting into the LLM at least: a contextual prompt, the contextual prompt providing to the LLM at least one or more features of the database, an expected structure of the executable query, and one or more example natural language requests and corresponding executable query outputs, and the natural language request; execute the executable query for retrieving the portion of the telematics data from the 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 plurality of databases.

A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.

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.

Systems and methods are provided for improved fleet management. For example, disclosed embodiments access toll transaction data received from one or more toll agencies, normalize the toll transaction data from each toll agency, access telematic data, normalize the telematic data, and generate a correlated trip dataset comprising the normalized toll transaction data and the normalized telematic data. Based on the correlated trip dataset, systems are configured to calculate a net present toll value, identify toll bill errors, identify pre-violations, and identify vehicle fraud and abuse.

A method of vehicle accident response includes generating, by a mobile device located inside a vehicle, telematics data associated with operation of the vehicle, and transmitting, from the mobile device to one or more processors via wireless communication, the telematics data. The method also includes analyzing the telematics data to determine that an accident involving the vehicle has occurred, analyzing the telematics data to determine a likelihood of injury due to the accident, and generating a communication (i) based upon the telematics data and the likelihood of injury and (ii) in response to determining that the accident has occurred. The method further includes transmitting, to the mobile device, the communication.

A method for monitoring emissions of vehicles includes receiving vehicle telematics data and fuel purchase data for the vehicles. The method includes formatting the telematics data and the fuel purchase data according to a data structure schema and storing the telematics data and the fuel purchase data in a data structure. The method includes determining if the telematics data or the fuel purchase data contains sufficient information to determine an amount of emissions generated by the vehicle(s) over a period of time and in response to determining that one of the vehicle telematics data or the fuel purchase data does not contain sufficient information to determine the emissions generated by the vehicle(s) over a particular period of time, utilizing, at least a portion of the other of the vehicle telematics data or the fuel purchase data to determine the emissions generated by the vehicle(s) over a particular period of time.

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.

A computer-implemented method includes: receiving a set of unlabeled telematics data; identifying a first set of baseline operation features; identifying a second set of baseline operation features; identifying a set of representative operation features associated with the unlabeled vehicular trip; generating a first set of baseline operation feature vectors, a second set of baseline operation feature vectors, and a set of representative operation feature vectors; classifying the unlabeled vehicular trip as a work trip when the set of representative operation feature vectors deviate from the first set of baseline operation feature vectors by less than a first deviation threshold; and classifying the unlabeled vehicular trip as a personal trip when the set of representative operation feature vectors deviates from the second set of baseline operation feature vectors by less than a second deviation threshold. Other embodiments are described.

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.

A method for monitoring emissions of vehicles includes receiving vehicle telematics data and fuel purchase data for the vehicles. The method includes formatting the telematics data and the fuel purchase data according to a data structure schema and storing the telematics data and the fuel purchase data in a data structure. The method includes determining if the telematics data or the fuel purchase data contains sufficient information to determine an amount of emissions generated by the vehicle(s) over a period of time and in response to determining that one of the vehicle telematics data or the fuel purchase data does not contain sufficient information to determine the emissions generated by the vehicle(s) over a particular period of time, utilizing, at least a portion of the other of the vehicle telematics data or the fuel purchase data to determine the emissions generated by the vehicle(s) over a particular period of time.

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.

Methods and systems are described for generating a vehicle-to-vehicle traffic alert and updating a vehicle-usage profile. Various aspects include detecting, via one or more processors associated with a first vehicle, that an abnormal traffic condition exists in an operating environment of the first vehicle. An electronic message is generated and transmitted wirelessly, via a vehicle-mounted transceiver associated with the first vehicle, to alert a nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition. The first vehicle receives telematics data regarding operation of the nearby vehicle after the nearby vehicle received the electronic message, and transmits the telematics data to a remote server for updating a vehicle-usage profile associated with the nearby vehicle.

The present invention relates to a polyester resin composition and a molded article manufactured using the same. According to the present invention, hydrolysis resistance may be improved even under high-temperature and high-humidity conditions. In addition, deviation in laser beam transmittance may be minimized even when the thickness of an injection-molded article is changed, thereby achieving laser weldability. Accordingly, the polyester resin composition of the present invention may be applied to small cameras, radomes, automobile electric / electronic equipment, or telematics parts.

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.

An information handling system may include a host system; and a management controller comprising a firmware that includes a bootloader component that is cryptographically signed by a manufacturer of the information handling system and a runtime component that is not cryptographically signed by the manufacturer of the information handling system. The management controller may be configured to establish trust with a remote information handling system by: receiving a handshake request from the remote information handling system, the handshake request including a payload; encrypting the payload via a key that is accessible by the bootloader component but not accessible by the runtime component; and responding to the handshake request by transmitting the encrypted payload to the remote information handling system.