How MTData constructed a CVML car telematics and driver monitoring resolution with AWS IoT


Constructing an IoT gadget for an edge Laptop Imaginative and prescient and Machine Studying (CVML) resolution could be a difficult endeavor. It’s worthwhile to compose your gadget software program, ingest video and pictures, prepare your fashions, deploy them to the sting, and handle your gadget fleet remotely. This all must be carried out at scale, and sometimes whereas dealing with different constraints equivalent to intermittent community connectivity and restricted edge computing assets. AWS companies equivalent to AWS IoT Greengrass, AWS IoT Core, and Amazon Kinesis Video Streams will help you handle and overcome these challenges and constraints, enabling you to construct your options sooner, and accelerating time to market.

MTData, a subsidiary of Telstra, designs and manufactures revolutionary car telematics and linked fleet administration expertise and options.MTData logo These options assist companies enhance operational effectivity, cut back prices, and meet compliance necessities. Its new 7000AI product represents a big advance in its product portfolio; a single gadget that mixes conventional regulatory telematics features with new superior video recording and pc imaginative and prescient options. Video monitoring of drivers permits MTData’s clients to cut back operational threat by measuring driver focus and by figuring out driver fatigue and distraction. Along with the MTData “Hawk Eye” software program, MTData’s clients can monitor their car fleet and driver efficiency, and determine dangers and traits.

The 7000AI gadget is bespoke {hardware} and software program. It screens drivers by performing CVML on the edge and ingests video to the cloud in response to occasions equivalent to detecting that the motive force is drowsy or distracted. MTData used AWS IoT companies to construct this superior telematics and driver monitoring resolution.

“By utilizing AWS IoT companies, significantly AWS IoT Greengrass and AWS IoT Core, we had been in a position to spend extra time on growing our resolution, moderately than spend time build up the advanced companies and scaffolding required to deploy and keep software program to edge gadgets with typically intermittent connectivity. We additionally get safety and scalability out of the field, which is vital as we’re coping with doubtlessly delicate information.

Amazon Kinesis Video Streams has additionally been a useful service, because it permits us to ingest video securely and cost-effectively, after which serve it again to the client in a really versatile means, with out the necessity to handle the underlying infrastructure.” – Brad Horton, Resolution Architect at MTData.


Structure Overview

MTData’s resolution consists of their 7000AI gadget, their “Hawk-Eye” software for car location and telemetry information, and their “Occasion Validation” software to overview and assess detected occasions and related video clips.

MTData architecture

Determine 1: Excessive-level structure of the 7000AI gadget and Hawk-Eye resolution

Let’s discover the steps within the MTData resolution, as proven in Determine 1.

  1. MTData deploys AWS IoT Greengrass on the 7000AI in-vehicle gadget to carry out CVML on the edge.
  2. Telemetry and GPS information from sensors on the car is shipped to AWS IoT Core over a mobile community. AWS IoT Core sends the info to downstream purposes primarily based on AWS IoT guidelines.
  3. The Hawk-Eye software processes telemetry information and reveals a dashboard of the car’s location and the sensor information.
  4. CVML fashions deployed on the edge on the 7000AI gadget are used to repeatedly analyze a video feed of the motive force. When the CVML mannequin detects that the motive force is drowsy or distracted, an alert is raised and a video clip of the detected occasion is shipped to Amazon Kinesis Video Streams for additional evaluation within the AWS cloud.
  5. The Occasion Validation software permits customers to validate and handle detected occasions. It’s constructed with AWS serverless applied sciences, and consists of the Occasion Processor and Occasion Evaluation parts, and an online software.
  6. The Occasion Processor is an AWS Lambda operate which receives and processes telemetry information. It writes real-time information to Amazon DynamoDB, analytical information to Amazon Easy Storage Service (Amazon S3), and forwards occasions to the Information Ingestion layer.
  7. The Information Ingestion layer consists of companies operating on Amazon Elastic Container Service (Amazon ECS) utilizing AWS Fargate, which ingests detected occasions and forwards them to the Hawk-Eye software.
  8. The Occasion Evaluation element gives entry to the detected occasion movies through an API, and consists of shoppers which learn detected occasion movies from Amazon Kinesis Video Streams.
  9. The front-end net software, hosted in Amazon S3 and delivered through Amazon CloudFront, permits customers to overview and handle distracted driver occasions.
  10. Amazon Cognito gives consumer authentication and authorization for the purposes.
MTData Event Validation

Determine 2: An occasion displayed within the Occasion Validation software

System Software program Composition

The 7000AI gadget is a bespoke {hardware} design operating an embedded Linux distribution on NVIDIA Jetson. MTData installs the AWS IoT Greengrass edge runtime on the gadget, and makes use of it to compose, deploy, and handle their IoT/CVML software. The applying consists of a number of MTData customized AWS IoT Greengrass parts, supplemented by pre-built AWS-provided parts. The customized parts are Docker containers and native OS processes, delivering performance equivalent to CVML inference, Digital Video Recording (DVR), telematics and configuration settings administration.

MTData Device Software Composition

Determine 3: 7000AI gadget software program structure

System Administration

AWS IoT Greengrass deployments are used to replace the 7000AI software software program. This deployment characteristic handles the intermittent connectivity of the mobile community; pausing deployment when disconnected, and progressing when linked. Quite a few deployment choices can be found to handle your deployments at scale.

Working system picture updates

There could be complication and threat related to updating an embedded Linux gadget by updating particular person packages. Dependency conflicts and piece-meal rollbacks have to be dealt with, to stop “bricking” a distant and hard-to-access gadget. Consequently, to cut back threat, updates to the embedded Linux working system (OS) of the 7000AI gadget are as a substitute carried out as picture updates of your entire OS.

OS picture updates are dealt with in a customized Greengrass element. When MTData releases a brand new OS picture model, they publish a brand new model of the element, and revise the AWS IoT Greengrass deployment to publish the change. The element downloads the OS picture file, applies it, reboots the gadget to provoke the swap of the energetic and inactive reminiscence banks, and run the brand new model. AWS IoT Greengrass configuration and credentials are held in a separate partition in order that they’re unaltered by the replace.

Edge CVML Inference

CVML inference is carried out at common intervals on photos of the car driver. MTData has developed superior CVML fashions for detecting occasions by which the motive force seems to be drowsy or distracted.

MTData Distracted Driver

Determine 4: Annotated video seize of a distracted driver occasion

Video Ingestion

The gadget software program contains the Amazon Kinesis Video Streams C++ Producer SDK. When MTData’s customized CVML inference detects an occasion of curiosity, the Producer SDK is used to publish video information to the Amazon Kinesis Video Streams service within the cloud. Consequently, MTData saves on bandwidth and prices, by solely ingesting video when there may be an occasion of curiosity. Video frames are buffered on gadget in order that the ingestion is resilient to mobile community disruptions. Video fragments are timestamped on the gadget, so delayed ingestion doesn’t lose timing context, and video information could be revealed out of order.

Video Playback

The Occasion Validation software makes use of the Amazon Kinesis Video Streams Archived Media API to obtain video clips or stream the archived video. Segments of clips can be spliced from the streamed video, and archived to Amazon S3 for subsequent evaluation, ML coaching, or buyer retention functions.


The AWS IoT System Shadow service is used to handle settings equivalent to inference on/off, live-stream on/off and digital camera video high quality settings. Shadows decouple the Hawk-Eye and the Occasion Validation purposes from the gadget, permitting the cloud purposes to change settings even when the 7000AI gadget is offline.


MTData developed an MLOps pipeline to help retraining and enhancement of their CVML fashions. Utilizing beforehand ingested video, fashions are retrained within the cloud, with the assistance of the NVIDIA TAO Toolkit. Up to date CVML inference fashions are revealed as AWS IoT Greengrass parts and deployed to 7000AI gadgets utilizing AWS IoT Greengrass deployments.

MTData MLOps pipeline

Determine 5: MLOps pipeline


By utilizing AWS companies, MTData has constructed a sophisticated telematics resolution that screens driver habits on the edge. A key functionality is MTData’s customized CVML inference that detects occasions of curiosity, and uploads corresponding video to the cloud for additional evaluation and oversight. Different capabilities embody gadget administration, working system updates, distant settings administration, and an MLOps pipeline for steady mannequin enchancment.

“Know-how, particularly AI, is advancing at an ever-increasing fee. We want to have the ability to preserve tempo with that and proceed to supply industry-leading options to our clients. By using AWS companies, we have now been in a position to proceed to replace, and enhance our edge IoT resolution with new options and performance, with out a big upfront monetary funding. That is necessary to me not solely to encourage experimentation in growing options, but additionally enable us to get these options to our edge gadgets sooner, extra securely, and with higher reliably than we may beforehand.” – Brad Horton, Resolution Architect at MTData.

To study extra about AWS IoT companies and options, please go to AWS IoT or contact us. To study extra about MTData, please go to their web site.

In regards to the authors

Greg BreenGreg Breen is a Senior IoT Specialist Options Architect at Amazon Net Providers. Based mostly in Australia, he helps clients all through Asia Pacific to construct their IoT options. With deep expertise in embedded methods, he has a specific curiosity in aiding product improvement groups to deliver their gadgets to market.
Ai-Linh LeAi-Linh Le is a Options Architect at Amazon Net Providers primarily based in Sydney, Australia. She works with telco clients to assist them construct options and resolve challenges. Her areas of focus embody telecommunications, information analytics and AI/ML.
Brad HortonBrad Horton is a Resolution Architect at Cellular Monitoring and Information (MTData), primarily based in Melbourne, Australia. He works to design and construct scalable AWS Cloud options to help the MTData telematics suite, with a specific concentrate on Edge AI and Laptop Imaginative and prescient gadgets.


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