Tag Archives: AWS IoT FleetWise

Software-defined edge architecture for connected vehicles

Post Syndicated from James Simon original https://aws.amazon.com/blogs/architecture/software-defined-edge-architecture-for-connected-vehicles/

To remain competitive in a marketplace that increasingly views transportation as a service emphasizing customer experience, vehicle capabilities and mobility applications need to improve and increase value over time, much like the internet of things and smart phones have done.

Vehicle manufacturers and fleet operators are responding to this change by using data to inform and operate their businesses, and by adopting software-defined vehicle features and capabilities. In the broader transportation community, insurance providers are evolving to usage-based insurance, which offers rates that are based on a customer’s risk profile rather than a general population. Telematics service providers are expanding their offerings to fleet operators to include machine learning (ML)-driven capabilities like driver coaching, compliance, and predictive maintenance. Rental car providers are developing apps that provide a personalized in-vehicle experience.

All of these features rely on in-vehicle data collection and processing, evolving vehicles from simple data gathering sensors to fully smart devices. To meet these demands, you can adopt cloud native architectures that use microservices, containers, and declarative application programming interfaces (APIs). This blog post explores a system architecture AWS and Luxsoft have developed together in order to help our customers reduce friction and accelerate time to market for the development, deployment and operation of edge applications required to make vehicles into smart devices.

Software-defined edge

Software is becoming more critical to vehicle function. A modern car has approximately 70-100 Electronic Control Units (ECUs), which control most core functions in the engine, transmission, Heating, ventilation, and air conditioning (HVAC) Automatic Brake System (ABS), body, and airbag hardware components. With new features such as infotainment systems, autonomous driving (AD), and advanced driver assistance systems (ADAS), modern cars use approximately 100 million lines of code, and this is increasing rapidly. ECU and software complexity produce challenges with portability of applications, this could be due to variations of the hardware, or CAN and other network communication differences, without an abstraction layer, application software must be conformed to the operating environment.

One difficulty presented by this increasing complexity and the multitude of integration points and communication interfaces at the edge is that applications typically must be re-developed, customized or at least cross-compiled to fit each hardware platform, often requiring a lengthy integration development and testing cycle for each new device. As a result, edge applications executing ML and other data-driven workloads can produce a pace that is slower than consumer expectations.

The development pace for software-defined vehicles (SDVs) relies on reusing and redeploying software applications. Software reuse is a challenge when target hardware and processing environments aren’t designed in a common way. Therefore, an early step in developing SDVs is to address these challenges at the vehicle edge, that is to say, the externally connected vehicle devices.

System architecture

To help address the challenges of creating reusable software and applications for the vehicle edge, we worked with AWS Partner DXC Luxsoft to create an end-to-end system architecture. The architecture in Figure 1 uses software-defined mobility devices at the vehicle edge to connect to multiple hardware components.

AWS Partner DXC Luxsoft to create an end-to-end system architecture

Figure 1. AWS Partner DXC Luxsoft to create an end-to-end system architecture

Let’s explore this architecture step by step.

  1. Edge application Developers check in source code to a CI/CD system for build and test. Applications are built as containers and tested on Amazon EC2 Elastic Compute Cloud (Amazon EC2) instances with target same Amazon Machine Images (AMI).
  2. Edge application containers are stored in Amazon Elastic Container Registry (Amazon ECR).
  3. Containers are registered as AWS IoT Greengrass components for target devices.
  4. Deploy edge applications to multiple target types using AWS IoT Greengrass.
  5. Data is published back to AWS through AWS IoT Core basic telemetry or AWS IoT Fleetwise.
  6. AWS IoT Core routes data to connected vehicle databases and services for further processing. Alternatively, AWS IoT Fleetwise-collected data is routed through AWS IoT Fleetwise.
  7. The Fleet Management portal allows fleet managers to view results produced by applications and services and data collected by AWS IoT Fleetwise. This can include geolocation, vehicle health, Usage Based Insurance (UBI), or other driver scores.
  8. Mobile clients can be created to allow end users and consumers to view application and service results and scores for applications like UBI.

How it works

This technology stack abstracts the specific hardware used, presenting a common run environment that you can deploy to devices that are based on ARM processors with ARM Sytem Ready firmware, including Amazon EC2 Graviton instances.

This architecture uses the following:

  • Device software stack with an ARM System Ready board support package
  • Yocto custom Linux build with the SOAFEE EWAOL layer(s) and AWS IoT Greengrass
  • Instance of the AWS IoT Fleetwise edge agent
  • Application containers for pre and post processing of data collected by AWS IoT Fleetwise or other applications

We paired this stack with AWS IoT Greengrass V2 and a container-based version of AWS IoT Fleetwise edge agent that we been modified to publish to a local broker first to allow pre and post-processing. This also makes AWS IoT Fleetwise-collected data available to other application containers running on the device. The pre- and post-processing containers prepare messages and data for use by other edge applications or exchange with the cloud services and will be available with the architecture source code.

Benefits

Because this architecture is container-based and abstracts the hardware, deploying and updating applications becomes more efficient. Because Amazon EC2 Graviton instances are ARM-based, you can deploy specific AMIs and configurations that contain this architecture’s technology stack as part of a continuous integration and continuous deployment (CI/CD) pipeline. This means that you can develop new applications and services entirely in the cloud, test them in the cloud with bit equivalent binaries and containers, and then deploy them to hardware components. This can save weeks to months of development and verification through the use of automation. The use of ARM based systems cloud native development and testing strategies can be applied, reducing the need for hardware test equipment and bringing new revenue streams and customer experiences to market at a pace that matches the current demand.

Conclusion

With this architecture, you can develop and deliver new edge processing applications to one or more vehicle device platforms. You can also develop, test, and deploy purpose-built edge compute applications as containerized AWS IoT Greengrass V2 components, including applications like Usage Based Insurance, over the air update agents, and driver distraction.

With this architecture alone in place, value can quickly be added by developing AWS IoT Fleetwise data campaigns targeting specific data required to fulfill business value for consumers, operators or fleet managers, such as vehicle battery state of charge or health indicators.

You can add value to this architecture by developing AWS IoT Fleetwise data campaigns for specific data required to fulfill business value for consumers, operators, or fleet managers. Examples of this data include vehicle battery state of charge and health indicators.

If you’re interested in creating or contributing to a common architecture that can accelerate developing and deploying edge applications to multiple hardware components, contact Connected Vehicle Tech Strategy Lead James Simon or AWS Partner DXC Luxsoft for a working demonstration or to start a proof of concept.

AWS IoT FleetWise Now Generally Available – Easily Collect Vehicle Data and Send to the Cloud

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-iot-fleetwise-now-generally-available-easily-collect-vehicle-data-and-send-to-the-cloud/

Today we announce the general availability of AWS IoT FleetWise, a fully managed AWS service that makes it easier to collect, transform, and transfer vehicle data to the cloud. Last AWS re:Invent 2021, we previewed AWS IoT FleetWise, heard customer feedback, and improved features for various use cases of near-real-time vehicle data processing.

With AWS IoT FleetWise, automakers, fleet operators, and automotive suppliers can take the complex variability out of collecting data from vehicle fleets at scale. You can access standardized fleet-wide vehicle data and avoid developing custom data collection systems, or you can integrate AWS IoT FleetWise to enhance your existing systems. AWS IoT FleetWise enables intelligent data collection that sends the exact data you need from the vehicle to the cloud. You can use the data to analyze vehicle fleet health to more quickly identify potential maintenance issues or make in-vehicle infotainment systems smarter. Furthermore, you can use it to train machine learning (ML) models that improve autonomous driving and advanced driver assistance systems (ADAS).

For example, electric vehicle (EV) battery temperature is a critical metric that should be continuously analyzed for the entire vehicle fleet. In order to avoid costly continuous data ingestion, you may want to optimize the data collection by setting a threshold on EV battery temperature. The results of this analysis would be provided to the automaker’s quality engineering department, enabling fast assessment of the criticality and possible root causes of any issues identified at certain temperatures. Based on the root cause analysis, the automaker can then take short-term actions to support the driver affected by the issue, as well as midterm actions to improve vehicle quality.

How AWS IoT FleetWise Works
AWS IoT FleetWise provides a vehicle modeling framework that you can use to model your vehicle and its sensors and actuators in the cloud. To enable secure communication between your vehicle and the cloud, AWS IoT FleetWise also provides the AWS IoT FleetWise Edge Agent application that you can use to download and install in-vehicle electronic control units (ECUs) such as the gateway, in-vehicle infotainment controller, etc. You define data collection schemes in the cloud and deploy them to your vehicle.

The AWS IoT FleetWise Edge Agent running in your vehicle uses data collection schemes to control what data to collect and when to transfer it to the cloud. Data collected and ingested through AWS IoT FleetWise Edge Agent software goes directly into your Amazon Timestream table or Amazon Simple Storage Service (Amazon S3) repositories via AWS IoT Core.

AWS IoT FleetWise Features
To get started with AWS IoT FleetWise, you can register your account and configure the settings via the AWS console. AWS IoT FleetWise automatically registers your AWS account, IAM role, and Amazon Timestream resources.

The Edge Agent software is a C++ application distributed as source code and is available on GitHub to collect, decode, normalize, cache, and ingest vehicle data to AWS. It supports multiple deployment options, such as vehicle gateways, infotainment systems, telematics control units (TCUs), or aftermarket devices. When vehicles are connected to the cloud, the Edge Agent continually receives data collection schemes and collects, decodes, normalizes and ingests the transformed vehicle data to AWS.

Let’s see the benefits and features of AWS IoT FleetWise:

Signal catalog
A signal catalog contains a collection of vehicle signals. Signals are fundamental structures that you define to contain vehicle data and its metadata. A signal can be a sensor and its status, an attribute as static information of the manufacturer, a branch to represent a nested structure such as Vehicle.Powertrain.combustionEngine expression, or an actuator such as the state of a vehicle device. For example, you can create a sensor to receive in-vehicle temperature values and store its metadata, including a sensor name, a data type, and a unit.

Signals in a signal catalog can be used to model vehicles that use different protocols and data formats. For example, there are two cars made by different automakers: one uses the Controller Area Network (CAN) to transmit the in-vehicle temperature data and the other uses On-board Diagnostic (OBD) protocol.

You can define a sensor in the signal catalog to receive in-vehicle temperature values. This sensor can be used to represent the thermocouples in both cars, irrespective of how this temperature data is available within the vehicle networks. For more information, see Create and manage signal catalogs in the AWS documentation.

Vehicle models
Vehicle models are virtual declarative representations that standardize the format of your vehicles and define relationships between signals in the vehicles. Vehicle models enforce consistent information across multiple vehicles of the same type so that you can quickly configure and create a vehicle fleet. In each vehicle model, you can add signals, including attributes, branches (signal hierarchies), sensors, and actuators.

You can define condition-based schemes to control what data to collect, such as data in-vehicle temperature values that are greater than 40 degrees. You can also define time-based schemes to control how often to collect data. For more information, see Create and manage vehicle models in the AWS documentation.

When a decoder manifest is associated with a vehicle model, you can create a vehicle. Each vehicle corresponds to an AWS IoT thing. You can use an existing AWS IoT thing to create a vehicle or set AWS IoT FleetWise to automatically create an AWS IoT thing for your vehicle. For more information, see Provision vehicles in the AWS documentation. After you create vehicles, you can create campaigns for them.

Campaigns
A campaign gives the AWS IoT FleetWise Edge Agent instructions on how to select, collect, and transfer data to the cloud. You can make a campaign with vehicle attributes that you added when creating vehicles, and a data collection scheme. You can manually define the data collection scheme either condition-based logical expressions such as $variable.myVehicle.InVehicleTemperature > 40.0, or time-based data collection in milliseconds such as from 10000 – 60000 milliseconds. To learn more, see Create a campaign in the AWS documentation.

After you create and approve the campaign, AWS IoT FleetWise automatically deploys the campaign to the listed vehicles. The AWS IoT FleetWise Edge Agent software doesn’t start collecting data until a running campaign is deployed to the vehicle. If you want to pause collecting data from vehicles connected to the campaign, on the Campaign summary page, choose Suspend. To resume collecting data from vehicles connected to the campaign, choose Resume.

Demo – Visualizing Vehicle Data
Here is a demo that aims to show how AWS IoT FleetWise can make it easy to collect vehicle data and use it to build visualizing applications. In this demo, you can simulate two kinds of vehicles, an NXP GoldBox powered by an Automotive Grade Linux distribution that runs the AWS IoT FleetWise agent as an AWS IoT Greengrass component or a completely virtual vehicle implemented as an AWS Graviton ARM-based Amazon EC2 instance. To learn more, see the getting started guide and source code in the GitHub repository.

The vehicle in CARLA Simulator can self-drive or be driven with a game steering wheel connected to your desktop. You can watch a live demo video.

Data is collected by AWS IoT FleetWise and stored in the Amazon Timestream table, and visualized on a Grafana Dashboard.

Customer and Partner Voices
During the preview period, we heard lots of feedback from our customers and partners in automotive industry such as automakers, fleet operators, and automotive suppliers.

For example, Hyundai Motor Group (HMG) is a global vehicle manufacturer that offers consumers a technology-rich lineup of cars, sport utility vehicles, and electrified vehicles. HMG has used AWS services, such as using Amazon SageMaker, to reduce its ML model training time for autonomous driving models.

Hae Young Kwon, vice president and head of the infotainment development group at HMG, said:

“As a leading global vehicle manufacturer, we have come to appreciate the breadth and depth of AWS services to help create new connected vehicle capabilities. With more data available from our expanding global fleet of connected cars, we look forward to leveraging AWS IoT FleetWise to discover how we can build more personalized ownership experiences for our customers.”

LG CNS is a global IT service provider and AWS Premier Consulting Partner that is transforming smart transportation services by building an advanced transportation system that is convenient and safe by maximizing the operational efficiency of multiple modes of transport, including buses, subways, taxis, railways, and airplanes.

Jae Seung Lee, vice president at LG CNS, said:

“At LG CNS, we are committed to advancing the technology that is powering the future of transportation. By using AWS IoT FleetWise, we are creating a new data platform that allows us to ingest, analyze, and simulate vehicle conditions in real-time. With these advanced insights, our customers can gain a better understanding of their vehicles and, as a result, improve decision-making about their fleets.”

Bridgestone is a global leader in tires and rubber building on its expertise to provide solutions for safe and sustainable mobility. Bridgestone has worked with AWS for several years to develop a system that delivers insights derived from the interaction between a tire and a vehicle using advanced machine learning capabilities on Amazon SageMaker.

Brian Goldstine, president of mobility solutions and fleet management at Bridgestone Americas Inc. said:

“Bridgestone has been working with AWS to transform the digital services we provide to our automotive manufacturer, fleet, and retail customers. We look forward to exploring how AWS IoT FleetWise will make it easier for our customers to collect detailed tire data, which can provide new insights for their products and applications.”

Renesas Electronics Corporation is a global leader in microcontrollers, analog, power, and system on chips (SoC) products. Renesas launched cellular-to-cloud IoT development platforms and its cloud development kits to run on AWS IoT Core and FreeRTOS.

Yusuke Kawasaki, director at Renesas Electronics Corporation, said:

“The volume of connected vehicle data is forecast to increase dramatically over the next few years, driven by new and evolving customer expectations. As a result, Renesas is focused on addressing the needs of automotive engineers facing increasing system complexity. Incorporating AWS IoT FleetWise into our vehicle gateway solution will enable our customers to enjoy our market-ready approach for large-scale data collection and accelerate their cloud development strategy. We look forward to further collaborating with AWS to provide a better and simpler development environment for our customers.”

By working with AWS IoT FleetWise Partners, you can take advantage of solutions to streamline your IoT projects, reduce the risk of your efforts, and accelerate time to value. To learn more how AWS accelerates the automotive industry’s digital transformation, see AWS for Automotive.

Now Available
AWS IoT FleetWise is now generally available in the US East (N. Virginia) and Europe (Frankfurt) Regions. You pay for the vehicles you have created and messages per vehicle per month. Additional services used alongside AWS IoT FleetWise, such as AWS IoT Core and Amazon Timestream, are billed separately. For more detail, see the AWS IoT FleetWise pricing page.

To learn more, see the AWS IoT FleetWise resources page including documentations, videos, and blog posts. Please send feedback to AWS re:Post for AWS IoT FleetWise or through your usual AWS support contacts.

– Channy