Geotab Patente

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Methods and systems 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 a reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The reversing-determination machine-learning model being trained by a vehicle reversing indication comprising a vehicle speed and a reverse gear indication.

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The present disclosure relates to systems and methods for analyzing health of vehicle batteries and vehicle electrical systems. Vehicle batteries, as well as other components of a vehicle electrical system, tend to degrade over time, and eventually fail. The described systems and methods generate an electrical system metric based on electrical data from a plurality of vehicles. Electrical system performance of individual vehicles is compared to the generated electrical system metric, to determine electrical system performance of vehicles relative to other similar vehicles.

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Systems and methods for validating telematics device installations are provided. The systems and methods involve operating at least one processor to: receive, from an installer, a device identifier corresponding to a telematics device; determine whether telematics data was received from the telematics device based at least on the device identifier; in response to determining that telematics data was received from the telematics device: present an indication to the installer that the telematics device was correctly installed in the vehicle; receive, from the installer, vehicle information associated with the vehicle; and store the vehicle information in association with the device identifier; and in response to determining that telematics data was not received from the telematics device: present an indication to the installer that the telematics device was not correctly installed in the vehicle; and store installation information in association with the device identifier.

Apparatus, device, methods and system relating to a vehicular telemetry environment for monitoring vehicle components and providing indications towards the rating condition of the vehicle components and providing optimal indications towards replacement or maintenance of vehicle components before vehicle component failure are disclosed.

A synchronized data set is generated from multiple asynchronous data sets. An interpolated data set is generated for each asynchronous data set, at a higher data rate than recorded data rates for the asynchronous data sets. The interpolated data for each data set is then sampled at a lower data rate which is common to all the interpolated data sets. The sampled data points are synchronized between the different data sets, such that the sampled data points represent a synchronized data set. Input telematic data can be reduced to inflection points in the telematic data, and interpolation and sampling of asynchronous data sets can be limited to threshold time periods around the inflection points.

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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.

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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.

Disclosed herein are systems and methods for identifying and ranking traffic bottlenecks. An example of such methods may include operating at least one processor to: receive traffic data associated with a road network comprising a plurality of road segments, the traffic data comprising vehicle speed data collected from a plurality of vehicles operating within the road network; determine a benchmark speed for each of the plurality of road segments; determine, for each of the plurality of road segments, whether a traffic disruption is present by comparing the benchmark speed thereof to vehicle speed data collected from at least one vehicle operating therealong; determine, for each of the plurality of road segments having the traffic disruption therealong, one or more road segment metrics associated therewith; identify a traffic bottleneck by aggregating a plurality of adjacent road segments having traffic disruptions therealong and the one or more road segment metrics associated therewith; and rank each traffic bottleneck based on one or more aggregated road segment metrics associated therewith.

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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.