Data-Centric Architecture Patterns - Software-Defined Vehicle

Data-Centric Architecture for SDV

Data-Centric vs Service-Centric

  SERVICE-CENTRIC (point-to-point)
  SpeedSensor --SOME/IP--> ADAS_ECU
  SpeedSensor --SOME/IP--> Instrument_Cluster
  SpeedSensor --SOME/IP--> OTA_Logger
  SpeedSensor --SOME/IP--> Cloud_Telemetry
  N consumers = N interfaces to maintain

  DATA-CENTRIC (broker pattern)
  SpeedSensor --publish--> [Data Broker] --subscribe--> ADAS_ECU
                                         --subscribe--> Instrument_Cluster
                                         --subscribe--> OTA_Logger
                                         --subscribe--> Cloud_Telemetry
  1 publish interface; N consumers self-register
  Adding new consumer: no change to SpeedSensor

  Eclipse Kuksa.val is the reference data broker implementing
  COVESA VSS as the data model and gRPC/WebSocket as transport

Eclipse Kuksa Data Broker Integration

Pythonkuksa_publisher.py

#!/usr/bin/env python3
"""Publish CAN speed signal to Kuksa data broker (VSS path)."""

import asyncio
import can
from kuksa_client.grpc import VSSClient, Datapoint

CAN_INTERFACE  = "can0"
CAN_SPEED_ID   = 0x1A3    # CAN frame ID for vehicle speed
KUKSA_ADDRESS  = "127.0.0.1"
KUKSA_PORT     = 55555
VSS_SPEED_PATH = "Vehicle.Speed"

def parse_speed_from_can(msg: can.Message) -> float:
    """Parse speed from CAN frame bytes 0-1 (0.01 km/h per bit)."""
    raw = (msg.data[0] << 8) | msg.data[1]
    return raw * 0.01  # scale to km/h

async def can_to_kuksa():
    bus = can.interface.Bus(CAN_INTERFACE, bustype="socketcan")

    async with VSSClient(KUKSA_ADDRESS, KUKSA_PORT) as client:
        print("CAN -> Kuksa bridge started")
        for msg in bus:
            if msg.arbitration_id == CAN_SPEED_ID:
                speed_kmh = parse_speed_from_can(msg)
                await client.set_current_values({
                    VSS_SPEED_PATH: Datapoint(speed_kmh)
                })

if __name__ == "__main__":
    asyncio.run(can_to_kuksa())

Vehicle Shadow Pattern

Component	Description	Implementation
Vehicle shadow	Cloud-side replica of vehicle state; always available even when vehicle offline	AWS IoT Thing Shadow, Azure Device Twin
Reported state	What the vehicle actually reports (current state)	Vehicle pushes via MQTT on change
Desired state	What the cloud/app wants to set (target state)	App writes desired; vehicle pulls on connect
Delta	Difference between desired and reported; vehicle must reconcile	Cloud sends delta to vehicle; vehicle executes
Last known state	Shadow retains last reported state for offline queries	Fleet dashboard shows last known position even if vehicle off

Summary

The data-centric architecture with a central broker is the pattern that enables the SDV app ecosystem. When an OEM publishes the VSS data broker API, third-party developers can write vehicle applications without access to the vehicle hardware or proprietary CAN databases -- they subscribe to standard VSS paths and receive real-time signal values. Eclipse Kuksa.val is the open-source reference implementation of this broker, and its adoption by Bosch, CARIAD, and Stellantis signals that it is becoming the de facto standard. The vehicle shadow pattern extends data-centricity to the cloud: the shadow provides a consistent, always-available view of vehicle state for cloud services, fleet management, and remote diagnostics -- regardless of whether the vehicle is currently online.

🔬 Deep Dive — Core Concepts Expanded

This section builds on the foundational concepts covered above with additional technical depth, edge cases, and configuration nuances that separate competent engineers from experts. When working on production ECU projects, the details covered here are the ones most commonly responsible for integration delays and late-phase defects.

Key principles to reinforce:

Configuration over coding: In AUTOSAR and automotive middleware environments, correctness is largely determined by ARXML configuration, not application code. A correctly implemented algorithm can produce wrong results due to a single misconfigured parameter.
Traceability as a first-class concern: Every configuration decision should be traceable to a requirement, safety goal, or architecture decision. Undocumented configuration choices are a common source of regression defects when ECUs are updated.
Cross-module dependencies: In tightly integrated automotive software stacks, changing one module's configuration often requires corresponding updates in dependent modules. Always perform a dependency impact analysis before submitting configuration changes.

🏭 How This Topic Appears in Production Projects

Project integration phase: The concepts covered in this lesson are most commonly encountered during ECU integration testing — when multiple software components from different teams are combined for the first time. Issues that were invisible in unit tests frequently surface at this stage.
Supplier/OEM interface: This is a topic that frequently appears in technical discussions between Tier-1 ECU suppliers and OEM system integrators. Engineers who can speak fluently about these details earn credibility and are often brought into critical design review meetings.
Automotive tool ecosystem: Vector CANoe/CANalyzer, dSPACE tools, and ETAS INCA are the standard tools used to validate and measure the correct behaviour of the systems described in this lesson. Familiarity with these tools alongside the conceptual knowledge dramatically accelerates debugging in real projects.

⚠️ Common Mistakes and How to Avoid Them

Assuming default configuration is correct: Automotive software tools ship with default configurations that are designed to compile and link, not to meet project-specific requirements. Every configuration parameter needs to be consciously set. 'It compiled' is not the same as 'it is correctly configured'.
Skipping documentation of configuration rationale: In a 3-year ECU project with team turnover, undocumented configuration choices become tribal knowledge that disappears when engineers leave. Document why a parameter is set to a specific value, not just what it is set to.
Testing only the happy path: Automotive ECUs must behave correctly under fault conditions, voltage variations, and communication errors. Always test the error handling paths as rigorously as the nominal operation. Many production escapes originate in untested error branches.
Version mismatches between teams: In a multi-team project, the BSW team, SWC team, and system integration team may use different versions of the same ARXML file. Version management of all ARXML files in a shared repository is mandatory, not optional.

📊 Industry Note

Engineers who master both the theoretical concepts and the practical toolchain skills covered in this course are among the most sought-after professionals in the automotive software industry. The combination of AUTOSAR standards knowledge, safety engineering understanding, and hands-on configuration experience commands premium salaries at OEMs and Tier-1 suppliers globally.