This project aims to develop an MQTT client that interfaces with the kuksa.val platform for accessing vehicle signals. The client will provide compatibility with AWS IoT leverage current standards for vehicle communication. Security will be enforced through mTLS authentication.

Definition of Done

Functional Requirements

  1. MQTT Client Implementation: The client must successfully interface with kuksa.val to access vehicle signals.
  2. AWS IoT Compatibility: A working reference implementation for AWS IoT is required, including device onboarding and data routing to Amazon Timestream DB.
  3. Compliance: The client must adhere to current VSS specifications.
  4. Vehicle Identification: The client must be able to resolve vehicle identification numbers or other unique identifiers.
  5. Protobuf: Payload definition for MQTT is defined.
  6. Configurability: The client should be adaptable to work with any MQTTv5 broker.
  7. AWS IoT Demo: A working demo with AWS IoT device onboarding, message routing to Amazon Timestream DB, and vehicle signal visualizations through Amazon Managed Grafana.

Non-Functional Requirements

  1. Documentation: Comprehensive documentation must exist, detailing the architecture, setup, and usage instructions, including steps for the AWS IoT demo.
  2. Unit Tests: All critical functionalities, including the AWS IoT demo, must be covered by unit tests.
  3. Peer Review: The code must be reviewed by at least two engineers and be approved.
  4. Security: mTLS should be used as the primary method for secure authentication.


Functional Requirements

  1. Language and Libraries: The client implementation should be done in C/C++, Python is an option as well.
  2. MQTT Protocol Version: The client must be compatible with MQTTv5.
  3. Cloud Architecture: Architecture details for interfacing with AWS IoT, including components for device onboarding and message routing to Amazon Timestream DB.
  4. Data Schema: Define a schema for vehicle signals based on VSS
  5. Protobuf Specifications: Focusing on signal updates.
  6. Configuration File: Design a user-configurable file for broker details and other settings.

Non-Functional Requirements

  1. Unit Testing Framework: Identify the framework and libraries to be used for unit testing.
  2. Documentation Standards: Establish standards and templates for documenting the code and architecture.
  3. Code Review Process: Outline the process and criteria for peer code reviews.
  4. Security Protocols: Specify the mTLS version and cipher suites that will be supported.

High level architecture 

Next Steps

  1. Migrate this document to Confluence
  2. Task Break Down: Start breaking down the work needed to deliver the mentioned DoD
    1. Interfaces definition
    2. Cloud breakdown
  3. Task Allocation: Assign specific components and tasks to team members, including tasks related to the AWS IoT demo.
  4. Development Timeline: Establish milestones and deadlines, ensuring that the demo is ready for a specified milestone.
    1. Embedded World Demo as a target
      1. Start in January
      2. Targeting March for vehicle part
      3. End by April
  5. Community Outreach: Strategies for involving the AGL community in project feedback and contributions.

Monday, October 30

Task Breakdown

Device Part:

  • Determine the Signals for Demonstration 
  • Description: Identify which CAN messages are available and decide which vehicle signals should be sent to the cloud for monitoring.
  • Owner: Nenad 
  • Timeline: 13th of November
  • Dependencies: None
  • Resources: CAN message logs, kuksa.val documentation
    • Nenad will get the list of signals from the the current FW demo and ask around about what are signals are used
  • Signal Forwarding Logic
  • Description: Decide the frequency or event triggers for forwarding the selected vehicle signals.
  • Owner: Scott
  • Timeline: [mid Jan - end Jan]
  • Dependencies: Determine the Signals for Demonstration
  • Resources: Engineering specifications
    • Decided to go with time based and trigger implementation located in a local config file
    • Write an example of the file for a specific usecase
  • Proto File Generation
  • Description: Generate a proto file that defines the messaging format
  • Owner: [Name/Role]
  • Timeline: [mid Jan - end Jan]
  • Dependencies: Signal Forwarding Logic
  • Resources: Protocol Buffers info
    • Define the timestamp 
    • identifier, do we need it to be part of the payload, or an MQTT topic, or we can simply use the MQTT client ID
    • This is a prerequisite for building out the cloud part of ingestion
  • Vehicle Onboarding
  • Description: Design and implement the mechanism for vehicle onboarding, including certificate generation and client ID definition.
  • Owner: Scott
  • Timeline: [end Jan - mid Feb]
  • Dependencies: None
  • Resources: AWS IoT documentation
    • How do we get a vehicle identifier (VIN)?
      • Populate values in VSS perhaps?
      • This should part of that static config
        • This should come from a TPM in a real world scenario
  • MQTT Device Client Development
  • Description: Develop the MQTT client to interface with kuksa.val and AWS IoT based on previous task findings.
  • Owner: Scott
  • Timeline: [end Feb - mid March]
  • Dependencies: Create Proto File, Vehicle Onboarding
  • Resources: MQTTv5 specifications, AWS SDK, other MQTT Clients

Cloud Components:

  • IaaC Setup
  • Description: Set up Infrastructure as Code for provisioning and managing cloud resources.
  • Owner: Nenad
  • Timeline: [Start Date - End Date]
  • Dependencies: None
  • Resources: AWS CloudFormation or CDK
  • Image Parser Component
  • Description: Develop a component to parse the vehicle signal images for cloud ingestion.
  • Owner: Nenad
  • Timeline: [23rd of Feb - 28th of Feb]
  • Dependencies: IaaC Setup, Device part: Proto File generation
  • Resources: AWS Lambda
  • Time Stream Injector Component
  • Description: Implement a component to insert the parsed signals into the Amazon Time Stream database.
  • Owner: Nenad
  • Timeline: [28th of Feb - 5th of March]
  • Dependencies: Image Parser Component
  • Resources: Amazon Time Stream DB documentation
  • Time Stream Data Modeling
  • Description: Model the Time Stream database to accommodate the vehicle signals.
  • Owner: Nenad
  • Timeline: [5th of March - 14th of March]
  • Dependencies: Time Stream Injector Component
  • Resources: Data modeling tools
  • Grafana Setup for Data Visualization
  • Description: Set up Grafana to create dashboards for visualizing vehicle signals.
  • Owner: Nenad
  • Timeline: [ 14th of March - 20th of March]
  • Dependencies: Time Stream Data Modeling
  • Resources: Amazon Managed Grafana documentation
  • Integration Work
  • Description: Integration of device MQTT client and cloud components .
  • Owner: Nenad/Scott
  • Timeline: [start March - end March]
  • Dependencies: All other task
  • Resources: None
  • No labels


  1. Example of signals:

    BO_ 401 Engine: 8 Vector__XXX
     SG_ ThrottlePosition : 63|8@0+ (0.392157,0) [0|100.000035] "%" Vector__XXX
    BO_ 381 ABS: 8 Vector__XXX
     SG_ VehicleSpeed : 7|12@0+ (0.0625,0) [0|255.9375] "km / h"  ECM_HS,BCM_HS
     SG_ SteeringPosition : 23|12@0+ (0.0439453125,-90) [-90|89.9560546875] "deg"  ECM_HS,BCM_HS
     SG_ BrakePressure : 39|8@0+ (75,0) [0|19125] "kPa"  ECM_HS,BCM_HS
    BO_ 532 Transmission: 8 Vector__XXX
     SG_ Gear : 7|8@0+ (1,-1) [-1|6] ""  ECM_HS,BCM_HS
    BO_ 533 Airbag: 8 Vector__XXX
     SG_ CollisionIntensity : 7|12@0+ (24.4140625,0) [0|100000] "N"  ECM_HS,BCM_HS
    BO_ 534 IMU1: 8 Vector__XXX
     SG_ AccelerationX : 7|12@0+ (0.48828125,-1000) [-1000|1000] "m/s^2"  ECM_HS,BCM_HS
     SG_ AccelerationY : 23|12@0+ (0.48828125,-1000) [-1000|1000] "m/s^2"  ECM_HS,BCM_HS
     SG_ AccelerationZ : 39|12@0+ (0.48828125,-1000) [-1000|1000] "m/s^2"  ECM_HS,BCM_HS
    BO_ 535 IMU2: 8 Vector__XXX
     SG_ GyroscopeX : 7|12@0+ (0.48828125,-1000) [-1000|1000] "rad/s"  ECM_HS,BCM_HS
     SG_ GyroscopeY : 23|12@0+ (0.48828125,-1000) [-1000|1000] "rad/s"  ECM_HS,BCM_HS
     SG_ GyroscopeZ : 39|12@0+ (0.48828125,-1000) [-1000|1000] "rad/s"  ECM_HS,BCM_HS
    BO_ 536 GNSS: 8 Vector__XXX
     SG_ Latitude : 7|32@0+ (0.0000000419095158577,-90) [-90|90] "deg"  ECM_HS,BCM_HS
     SG_ Longitude : 39|32@0+ (0.00000008381903171539,-180) [-180|180] "deg"  ECM_HS,BCM_HS
  2. For those signals, the ones that look problematic ATM with VSS 4.0 are:

    • ThrottlePosition: There are a couple of options in the OBD part of the VSS tree, but there has been discussion around making the OBD signals optional.  It may be worthwhile looking at submitting a PR to add an equivalent non-OBD signal to upstream VSS for future use.
    • BrakePressure: Vehicle.Chassis.Brake.PedalPosition is 0-100 percent.  If there's a perceived need for a signal with the absolute force in kPa, then it is another potential upstream change (likely to add relative vs absolute as has been done with some other signals).
    • CollisionIntensity: Vehicle.Cabin.Seat.Row1.DriverSide.Airbag.IsDeployed is a boolean value, and I do not see anything that shows Ns or g's for collision or impact.  If this is commonly used, it's likely worth a discussion with upstream on where it should go.
  3. Next Steps:

    • Nenad Ilic to record the CARLA CAN messages based on the signal mentioned above
    • Scott Murray will look into Protobuff Generation :
      • Define the timestamp 
      • identifier, do we need it to be part of the payload, or an MQTT topic, or we can simply use the MQTT client ID
      • This is a prerequisite for building out the cloud part of ingestion
    • Nenad Ilic and Scott Murray to review the Protobuff generation initially via email and then sync call if necessary.
  4. Open Questions:

    • Where will the protobuf be living (gerrit location?)
    • Example application AGL-VSS-Proxy (git repo)? ->
    • AWS Cloud in the aws4embeddedlinux with instructions
    • Should we include this in the main demo platform?


    • Figure out the simulation of those signals

  5. I'll need to catch up some work while I was off. Will focus on 

    • Time Stream Injector Component and
    • Time Stream Data Modeling

    in the next couple of days