AWS IoT FleetWise FAQs


AWS IoT FleetWise is a managed service that allows you to efficiently collect vehicle data and organize it in the cloud to gain insights about your fleet(s) of vehicles. You can use the data transferred by AWS IoT FleetWise to help you analyze vehicle fleet health to quickly identify potential maintenance issues, make in-vehicle infotainment systems smarter, or use analytics and machine learning (ML) to improve models for autonomous driving and advanced driver assistance systems (ADAS).

AWS IoT FleetWise removes the complexities of collecting data from vehicle fleets at scale. Using virtual vehicle modeling, you can create a common data format across vehicle brands, models, and components, allowing for streamlined fleet-wide data analysis in the cloud.

AWS IoT FleetWise also helps you more intelligently collect vehicle data, which provides you access to more useful data in the cloud. You can improve data relevance by creating time- and event-based data collection campaigns that send the exact data you need to the cloud.

First, log in to the AWS Management Console. Next, check out the documentation for getting started, listed under the documentation tab on this page. Finally, start building with AWS IoT FleetWise by modeling your vehicle and defining data collection campaigns.

The Edge Agent Reference Implementation for AWS IoT FleetWise can run on most embedded Linux-based platforms. To see examples of reference hardware the Edge Agent Reference Implementation has been ported to, visit the Partners page. Instead of procuring hardware, the Developer Guide contains instructions for setting up a virtual environment using an Amazon Elastic Compute Cloud (EC2) instance powered by AWS Graviton2.

Porting your Edge Agent to your target hardware is a straightforward process. To get started, you can find porting information on the Edge Agent GitHub page, which includes source code access, platform support, build dependency information, and other resources. To learn more, see the Edge Agent guide available within the console.

Data collected and ingested through AWS IoT FleetWise goes directly into your storage repository, such as Amazon Timestream or Amazon Simple Storage Service (Amazon S3). You own and control data collected by AWS IoT FleetWise.

AWS IoT FleetWise collects and transports data collected from traditional CAN-based vehicle sensors (for instance, engine temperature or fuel pressure), as well as from vehicle sub-systems that include vision sensors like cameras, radars, and lidars.

A vehicle model is a digital representation of your vehicle’s sensors and signals. You can make these models as simple or as detailed as you wish. Using the virtual vehicle model as a blueprint for AWS IoT FleetWise allows you to organize vehicles by common attributes (such as number of doors) into campaigns and then set uniform collection parameters across your diverse fleet of vehicles. Vehicle models can help you create and share sensor and signal definitions across multiple providers. For detailed information, see the AWS IoT FleetWise Developer Guide.

Define the vehicle data you wish to collect by writing straight forward, rules-based statements from the AWS IoT FleetWise console. AWS IoT FleetWise sends these statements to your vehicles from the cloud, and your Edge Agent collects and transfers data according to the statements. For detailed information, see the AWS IoT FleetWise Developer Guide.

You can integrate AWS IoT FleetWise with your existing implementations of AWS services, such as AWS IoT Core and Amazon Timestream. To discuss integration with your specific architecture, or other integration options with AWS IoT FleetWise, contact our sales team.


Use the Developer Guide to learn about key concepts and instructions for using the service.

In the API Reference, you’ll learn about API operations in detail and see sample requests, responses, and errors for the supported web services protocols.

The Edge Agent GitHub page includes source code access, platform support, build dependency information, and other resources.

Data storage

  • Amazon Timestream is a fast, scalable, and serverless time series database. You might use Amazon Timestream when you need to feed near real time data into a dashboard to reflect current vehicle health status.
  • Amazon S3 is an object storage service offering industry-leading scalability, data availability, security, and performance. You might use it when you have a scenario that requires batch processing and analysis of vehicle data over a period of time. For example, when monitoring driving behaviors, tracking infotainment interactions, or creating better long-term maintenance plans for electric vehicle fleets.

No. To change the storage destination, you must create a new campaign and choose Amazon S3.

Vision system data

To collect vision system data using AWS IoT FleetWise, your vehicle hardware must send data signals via ROS2 (Robotic Operating System 2) middleware to your Edge Agent. CAN-based sensors can continue to send signals via the CAN bus. In 2024, we plan to support additional middleware.

Amazon Kinesis Video Streams lets you send video and audio directly from camera sensors to AWS for storage, playback, and analytics. With Kinesis Video Streams, customers can configure both continuous or events-based streaming. The new Kinesis Video Streams Edge Agent enables you to locally record and store video and audio from cameras and send media to the cloud on a defined schedule for long-term storage and playback. Additionally, using Kinesis Video Streams with WebRTC, you can enable real-time, on-demand viewing of camera media.

Vision system data from AWS IoT FleetWise lets you collect data from vehicle vision sub-systems and middleware that include cameras, radars, and lidars. In the cloud, you can model vehicle sensors and sub-systems, and define campaigns that let you collect only the data that you need (for example, only 5 seconds before and after a hard-braking event). The service automatically synchronizes and organizes structured and unstructured vision system data, metadata, and standard sensor (telemetry data), enabling you to run experiments, augment machine learning models, and improve simulations.

You will first model and provision your vehicles in the cloud. AWS IoT FleetWise provides scripts that enable you to import your signals to the signal catalog in ROS2 format and build a decoder that enables your Edge Agent to parse those signals. You will also be able to import CAN signals and specify their decoders, just as you can today.

Next, you will deploy a data collection campaign. You can create time- or event-based campaigns and collect both CAN signals and ROS2 signals in the same campaign. Your Edge Agent will collect data from the relevant vehicle middleware or network (ROS2/CAN) based on the campaign parameters. Both structured (object list, sensor metadata, vehicle ID) and unstructured (image or video frames) data will be sent to the cloud.

AWS IoT FleetWise will automatically organize the data by adding timestamps and other metadata (event ID, campaign, vehicle). Structured data will have a link to corresponding unstructured data stored in Amazon S3 for easy reference. Customers can use other AWS services like AWS Glue and Amazon Athena to combine their datasets and query their data.