Abstract: Described herein are various techniques, including a system that uses high-rate acceleration data for computing an accident score indicative of a potential collision and triggering an action in response to determining that the accident score indicates a potential collision. The system is configured to filter out undesired high-rate acceleration trigger events such as noise and harsh braking events prior to determining the accident score. The accident score is based on contexts or scores computed from high-rate acceleration data, speed, and GPS data captured by a telematics monitor deployed in a vehicle.

Abstract: Disclosed herein are methods for determining an estimated weight of a vehicle. The methods comprise operating at least one processor to: receive vehicle data associated with the vehicle, the vehicle data comprising a plurality of vehicle parameters collected during operation of the vehicle; identify one or more vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data; and use at least one machine learning model to determine the estimated weight of the vehicle based on the portion of the vehicle data associated with each of the one or more vehicle maneuvers, the at least one machine learning model trained using training data associated with a plurality of previous vehicle maneuvers. Also disclosed are systems for implementing methods of the present disclosure.

Abstract: Described herein are various techniques, including a method that uses high-rate acceleration data for computing an accident score indicative of a potential collision and triggering an action in response to determining that the accident score indicates a potential collision. The method includes filtering out undesired high-rate acceleration trigger events such as noise and harsh braking events prior to determining the accident score. The accident score is based on contexts or scores computed from high-rate acceleration data, speed, and GPS data captured by a telematics monitor deployed in a vehicle.

Abstract: 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.

Abstract: Described herein are various techniques, including systems and non-transitory instructions, that, in response to obtaining information regarding a potential collision between a vehicle and an object, obtain data describing the vehicle for a time period extending before and after a time of the potential collision. The system 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 system may trigger one or more actions responding to the potential collision.

Abstract: 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.

Abstract: Described herein are various techniques, including a method that uses high-rate acceleration data for computing an accident score indicative of a potential collision and triggering an action in response to determining that the accident score indicates a potential collision. The method includes filtering out undesired high-rate acceleration trigger events such as noise and harsh braking events prior to determining the accident score. The accident score is based on contexts or scores computed from high-rate acceleration data, speed, and GPS data captured by a telematics monitor deployed in a vehicle.

Abstract: 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.

Abstract: Systems and methods for determining an estimated weight of a vehicle are provided. The system includes at least one data storage and at least one processor. The at least one data storage is configured to store vehicle data associated with the vehicle. The at least one processor is configured to: identify a plurality of vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data, each portion of the vehicle data comprising a measured torque profile; generate a plurality of simulated torque profiles for each vehicle maneuver; generate a plurality of error profiles, an error profile being generated for each vehicle maneuver based on differences between the plurality of simulated torque profiles and the measured torque profile corresponding to that vehicle maneuver; and determine the estimated weight of the vehicle based on the plurality of error profiles.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for determining an estimated weight of a vehicle are provided. The system includes at least one data storage and at least one processor. The at least one data storage is configured to store vehicle data associated with the vehicle. The at least one processor is configured to: identify a plurality of vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data, each portion of the vehicle data comprising a measured torque profile; generate a plurality of simulated torque profiles for each vehicle maneuver; generate a plurality of error profiles, an error profile being generated for each vehicle maneuver based on differences between the plurality of simulated torque profiles and the measured torque profile corresponding to that vehicle maneuver; and determine the estimated weight of the vehicle based on the plurality of error profiles.

Abstract: Described herein are various techniques, including systems and non-transitory instructions, that, in response to obtaining information regarding a potential collision between a vehicle and an object, obtain data describing the vehicle for a time period extending before and after a time of the potential collision. The system 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 system may trigger one or more actions responding to the potential collision.

Abstract: 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.

Abstract: 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.

Abstract: 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.