METHODS FOR CHARACTERIZING A VEHICLE COLLISION

    公开(公告)号:US20220246036A1

    公开(公告)日:2022-08-04

    申请号:US17404816

    申请日:2021-08-17

    申请人: Geotab Inc.

    摘要: Described herein are examples of a computerized method that comprises: in response to obtaining information regarding a potential collision between a vehicle and an object, obtaining data describing the vehicle for a time period extending before and after a time of the potential collision. The method may determine a likelihood that the potential collision is a non-collision event based on the data describing the vehicle by performing one or more assessments. The assessments may include telematics monitor assessment, driver behavior assessment, road surface feature assessment, trip correlation assessment, and/or context assessment. In response to determining that the likelihood indicates that the potential collision is not a non-collision event, the method may trigger one or more actions responding to the potential collision.

    DATA CAPTURE INSTRUCTIONS FOR ASSET TRACKING

    公开(公告)号:US20210390497A1

    公开(公告)日:2021-12-16

    申请号:US17008106

    申请日:2020-08-31

    申请人: Geotab Inc.

    摘要: Methods, systems, and devices for data capture instructions for asset tracking are provided. An example method for capturing data involves obtaining raw data from a data source onboard an asset and monitoring the raw data for satisfaction of a simplified data capture trigger. When the simplified data capture trigger is satisfied, a dataset simplification algorithm is performed on the raw data to generate a simplified set of raw data, and the simplified set of raw data is logged. The method further involves monitoring the raw data for satisfaction of a rich data capture trigger. When the rich data capture trigger is satisfied, an unsimplified block of raw data is identified and logged for rich data analysis. The data is transmitted to a server. The unsimplified block of raw data contains raw data that is additional to the raw data contained in the simplified set of raw data.

    Method and system for impact detection in a stationary vehicle

    公开(公告)号:US11586269B1

    公开(公告)日:2023-02-21

    申请号:US17863888

    申请日:2022-07-13

    申请人: Geotab Inc.

    摘要: A method and a system for impact detection in a stationary vehicle are provided. The method includes putting a telematics device into a sleep mode and performing a first micro wakeup. In response to determining that a first value read from a sensor during the micro wakeup is greater than a noise threshold, increasing a frequency of the micro wakeups and a sampling rate of the sensor. The method also includes reading a second value from the sensor during a second wakeup, performing a regular wakeup, and sending the first and second values during the regular wakeup.

    DATA CAPTURE TRIGGER CONFIGURATION FOR ASSET TRACKING

    公开(公告)号:US20210389779A1

    公开(公告)日:2021-12-16

    申请号:US17008151

    申请日:2020-08-31

    申请人: Geotab Inc.

    摘要: Methods, systems, and devices for data capture trigger configuration for asset tracking are provided. Another example method capturing raw data involves obtaining a rich data capture trigger that defines when a controller of an asset tracking device onboard an asset is to identify and log an unsimplified block of raw data in raw data on the asset tracking device for rich data analysis, transmitting data capture instructions to the asset tracking device that contains the rich data capture trigger, and receiving the simplified set of raw data and the unsimplified block of raw data from the asset tracking device.

    SYSTEMS AND METHODS FOR VEHICLE REVERSING DETECTION USING EDGE MACHINE LEARNING

    公开(公告)号:US20230222849A1

    公开(公告)日:2023-07-13

    申请号:US18094571

    申请日:2023-01-09

    申请人: Geotab Inc.

    IPC分类号: G07C5/00 B60W30/18

    CPC分类号: G07C5/008 B60W30/18036

    摘要: Methods for reversing determination for a vehicle asset are provided. The methods include capturing by a telematics device coupled to the vehicle acceleration data from a three-axis accelerometer, determining by an edge reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The edge reversing-determination machine-learning model being updated based on a centralized reversing-determination machine-learning model trained using a vehicle-provided reversing indication.