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AWS IoT FleetWise provides a set of tools that enable automakers to collect, transform, and transfer vehicle data to the cloud at scale. With AWS IoT FleetWise you can build virtual representations of vehicle networks and define data collection rules to transfer only high-value data from your vehicles to AWS Cloud.
The Reference Implementation for AWS IoT FleetWise ("FWE") provides C++ libraries that can be run with simulated vehicle data on certain supported vehicle hardware or that can help you develop an Edge Agent to run an application on your vehicle that integrates with AWS IoT FleetWise. You can use AWS IoT FleetWise pre-configured analytic capabilities to process collected data, gain insights about vehicle health, and use the service's visual interface to help diagnose and troubleshoot potential issues with the vehicle.
AWS IoT FleetWise's capability to collect ECU data and store them on cloud databases enables you to utilize different AWS services, such as Analytics Services, and ML, to develop novel use-cases that augment and/or supplement your existing vehicle functionality. In particular, AWS IoT FleetWise can help utilize fleet data (Big Data) to create value. For example, you can develop use cases that optimize vehicle routing, improve electric vehicle range estimation, and optimize battery life charging. You can use the data ingested through AWS IoT FleetWise to develop applications for predictive diagnostics, and for outlier detection with an electric vehicle's battery cells.
You can use the included sample C++ application to learn more about the Reference Implementation, develop an Edge Agent for your use case and test interactions before integration.
This software is licensed under the Apache License, Version 2.0.
The Reference Implementation for AWS IoT FleetWise ("FWE") is intended to help you develop your Edge Agent for AWS IoT FleetWise and includes sample code that you may reference or modify so your Edge Agent meets your requirements. As provided in the AWS IoT FleetWise Service Terms, you are solely responsible for your Edge Agent, including ensuring that your Edge Agent and any updates and modifications thereto are deployed and maintained safely and securely in any vehicles.
This software code base includes modules that are still in development and are disabled by default. These modules are not intended for use in a production environment. This includes a Remote Profiler module that helps sending traces from the device to AWS Cloud Watch. FWE has been checked for any memory leaks and runtime errors such as type overflows using Valgrind. No issues have been detected during the load tests.
Note that vehicle data collected through your use of AWS IoT FleetWise is intended for informational purposes only (including to help you train cloud-based artificial intelligence and machine learning models), and you may not use AWS IoT FleetWise to control or operate vehicle functions. You are solely responsible for all liability that may arise in connection with any use outside of AWS IoT FleetWise's intended purpose and in any manner contrary to applicable vehicle regulations. Vehicle data collected through your use of AWS IoT FleetWise should be evaluated for accuracy as appropriate for your use case, including for purposes of meeting any compliance obligations you may have under applicable vehicle safety regulations (such as safety monitoring and reporting obligations). Such evaluation should include collecting and reviewing information through other industry standard means and sources (such as reports from drivers of vehicles). You and your End Users are solely responsible for all decisions made, advice given, actions taken, and failures to take action based on your use of AWS IoT FleetWise.
ubuntu: (Note: If connecting to the hostname
ubuntu doesn't work, find the IP address from your internet router instead.)ssh ubuntu@ubuntu
unzip:sudo apt update \
&& sudo apt upgrade -y \
&& sudo apt install -y unzip
sudo nano /boot/firmware/usercfg.txt
dtparam=spi=on
dtoverlay=mcp2515-can0,oscillator=16000000,interrupt=23
dtoverlay=mcp2515-can1,oscillator=16000000,interrupt=25
dtoverlay=spi-bcm2835-overlay
CTRL+O, CTRL+X) and reboot the Raspberry Pi (sudo reboot).These steps require an Ubuntu 20.04 development machine with 10 GB free disk space. If necessary, you can use a local Intel x86_64 (amd64) machine. We recommended using the following instructions to launch an AWS EC2 Graviton (arm64) instance. For more information about Amazon EC2 pricing, see Amazon EC2 On-Demand Pricing.
Sign in to your AWS account.
Open the Launch CloudFormation Template.
Enter the Name of an existing SSH key pair in your account from here.
.pem..pem file. Be sure to update the file permissions:
chmod 400 <PATH_TO_PEM>Select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
Choose Create stack. Wait until the status of the Stack is CREATE_COMPLETE. This can take up to five minutes.
Choose the Outputs tab, copy the EC2 IP address, and connect from your local machine through SSH to the development machine.
ssh -i <PATH_TO_PEM> ubuntu@<EC2_IP_ADDRESS>
Next, compile FWE for the ARM 64-bit architecture of the processor present in the Raspberry Pi.
On your development machine, clone the latest FWE source code from GitHub by running the following:
git clone https://github.com/aws/aws-iot-fleetwise-edge.git ~/aws-iot-fleetwise-edge \
&& cd ~/aws-iot-fleetwise-edge
Review, modify and supplement the FWE source code to ensure it meets your use case and requirements.
Install the FWE dependencies. The command below installs the following Ubuntu packages for compiling FWEfor ARM 64-bit:
libssl-dev libboost-system-dev libboost-log-dev libboost-thread-dev build-essential cmake unzip git wget curl zlib1g-dev libcurl4-openssl-dev libsnappy-dev default-jre libasio-dev.
Additionally, it installs the following: jsoncpp protobuf aws-sdk-cpp. (If you are using a
local x86_64 development machine, use the install-deps-cross-arm64.sh script instead.)
sudo -H ./tools/install-deps-native.sh
To compile your Edge Agent, run the following command. (If you are using a local x86_64
development machine, use the build-fwe-cross-arm64.sh script instead.)
./tools/build-fwe-native.sh
On the development machine, create an IoT Thing with the name fwdemo-rpi and provision its
credentials by running the following command. Your Edge Agent binary and its configuration files are
packaged into a ZIP file that is ready for deployment to the Raspberry Pi.
mkdir -p ~/aws-iot-fleetwise-deploy && cd ~/aws-iot-fleetwise-deploy \
&& cp -r ~/aws-iot-fleetwise-edge/tools . \
&& mkdir -p build/src/executionmanagement \
&& cp ~/aws-iot-fleetwise-edge/build/src/executionmanagement/aws-iot-fleetwise-edge \
build/src/executionmanagement/ \
&& mkdir -p config && cd config \
&& ../tools/provision.sh \
--vehicle-name fwdemo-rpi \
--certificate-pem-outfile certificate.pem \
--private-key-outfile private-key.key \
--endpoint-url-outfile endpoint.txt \
--vehicle-name-outfile vehicle-name.txt \
&& ../tools/configure-fwe.sh \
--input-config-file ~/aws-iot-fleetwise-edge/configuration/static-config.json \
--output-config-file config-0.json \
--log-color Yes \
--vehicle-name `cat vehicle-name.txt` \
--endpoint-url `cat endpoint.txt` \
--can-bus0 can0 \
&& cd .. && zip -r aws-iot-fleetwise-deploy.zip .
On your local machine, copy the deployment ZIP file from the machine with Amazon EC2 to your local machine by running the following command:
scp -i <PATH_TO_PEM> ubuntu@<EC2_IP_ADDRESS>:aws-iot-fleetwise-deploy/aws-iot-fleetwise-deploy.zip .
On your local machine, copy the deployment ZIP file from your local machine to the Raspberry Pi by running the following command:
scp aws-iot-fleetwise-deploy.zip ubuntu@ubuntu:
As described in step 4 of setting up the Raspberry Pi, connect through SSH to the Raspberry Pi. On the Raspberry Pi, install your Edge Agent as a service by running the following command:
mkdir -p ~/aws-iot-fleetwise-deploy && cd ~/aws-iot-fleetwise-deploy \
&& unzip -o ~/aws-iot-fleetwise-deploy.zip \
&& sudo mkdir -p /etc/aws-iot-fleetwise \
&& sudo cp config/* /etc/aws-iot-fleetwise \
&& sudo ./tools/install-fwe.sh
Install the can-isotp module:
sudo -H ~/aws-iot-fleetwise-deploy/tools/install-socketcan.sh
Run
sudo nano /usr/local/bin/setup-socketcan.sh
and add the following lines to bring up the can0 and can1 interfaces at startup:
ip link set up can0 txqueuelen 1000 type can bitrate 500000 restart-ms 100
ip link set up can1 txqueuelen 1000 type can bitrate 500000 restart-ms 100
Restart the setup-socketcan service and your Edge Agent service:
sudo systemctl restart setup-socketcan
sudo systemctl restart fwe@0
To verify your Edge Agent is running and is connected to the cloud, check the log file:
sudo journalctl -fu fwe@0 --output=cat
[INFO ] [AwsIotConnectivityModule.cpp:161] [connect()] [Connection completed successfully]
On the development machine, install the AWS IoT FleetWise demo script dependencies by running the
following commands. The script installs the following Ubuntu packages: python3 python3-pip, and
then installs the following PIP packages: wrapt plotly pandas cantools.
cd ~/aws-iot-fleetwise-edge/tools/cloud \
&& sudo -H ./install-deps.sh
On the development machine, deploy a heartbeat campaign that periodically collects OBD data by running the following commands:
./demo.sh --vehicle-name fwdemo-rpi --campaign-file campaign-obd-heartbeat.json
The demo script does the following:
obd-nodes.json. Next, it adds CAN signals in a flat signal list based on the DBC file
hscan.dbc.obd-decoders.json for decoding
OBD signals from the network interfaces defined in network-interfaces.json.hscan.dbc to the decoder manifest.ACTIVE.fwdemo-rpi, which is also the name passed to
provision.sh.campaign-obd-heartbeat.json. This contains a time-based collection
scheme that collects OBD data and targets the campaign at the fleet.HEALTHY, which means the campaign was deployed to the
fleet.If you enabled S3 upload destination by passing the option --enable-s3-upload, the demo script
will additionally:
campaign-brake-event.json. One campaign will upload data
to to S3 in JSON format, one to S3 in parquet formatWhen the script completes, you receive the path to the output HTML file on your local machine. To
download it, use scp, and then open it in your web browser:
scp -i <PATH_TO_PEM> ubuntu@<EC2_IP_ADDRESS>:<PATH_TO_HTML_FILE> .
To explore the collected data, click and drag on the graph to zoom in. Alternatively, if your AWS account is enrolled with QuickSight or Amazon Managed Grafana, you can use them to browse the data from Amazon Timestream directly.
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