Manifest Files & Configuration - AUTOSAR Adaptive Platform

Three Manifest Types

AUTOSAR Adaptive uses three distinct manifest types. Each is a JSON (or ARXML) file that is validated and deployed at runtime — not compiled into the binary.

Manifest	Scope	Key Contents
Application Manifest	Per-process	Process startup config, scheduling, Function Group state membership
Service Instance Manifest	Per service binding	SOME/IP service ID / instance ID, endpoint IP:port, E2E config
Machine Manifest	Per ECU	Function Groups, cgroup resource limits, NM config, trusted platform

Application Manifest Fields

JSONapplication_manifest.json

{
  "manifestFormatVersion": "1.0",
  "shortName": "SensorApp",
  "executable": {
    "path": "bin/SensorApp",
    "startupConfig": {
      "schedulingPolicy": "FIFO",
      "schedulingPriority": 50,
      "timingBudget": 5000,
      "startupTimeout": 3000,
      "shutdownTimeout": 2000
    }
  },
  "functionGroupStateMembership": [
    {
      "functionGroup": "SensorFG",
      "states": ["Active"]
    },
    {
      "functionGroup": "MachineFG",
      "states": ["Driving", "Parking"]
    }
  ],
  "requiredFunctionalClusters": ["ara::com", "ara::log", "ara::exec"]
}

⚠️ startupTimeout

startupTimeout is the window (ms) within which the process must call ReportApplicationState(kRunning). If this window is exceeded, EM sends SIGTERM. A common mistake is setting this too low for processes that perform heavy initialisation (loading a neural-network model, calibrating sensors).

Service Instance Manifest

JSONservice_instance_manifest.json

{
  "serviceInterfaceDeployments": [
    {
      "shortName": "SensorServiceDeployment",
      "serviceInterfaceRef": "/SensorApp/SensorServiceInterface",
      "binding": "SomeIp",
      "someIpServiceInstanceId": {
        "serviceId": "0x1234",
        "instanceId": "0x0001"
      },
      "networkEndpoint": {
        "networkAddress": "192.168.0.10",
        "port": 50001,
        "protocol": "UDP"
      },
      "events": [
        {
          "shortName": "ImuEvent",
          "eventId": "0x8001",
          "e2eProfile": "P04",
          "dataId": 42
        }
      ]
    }
  ]
}

💡 Instance Specifier Mapping

The ara::core::InstanceSpecifier path used in C++ code (e.g., SensorApp/SensorService/Instance0) is resolved against this manifest at runtime. The CM reads the manifest, looks up the SOME/IP service ID + instance ID, and uses that for SD Offer/Find. A mismatch between the shortName path and the C++ InstanceSpecifier is the most common "service not found" bug.

Machine Manifest

JSONmachine_manifest.json

{
  "machineDesign": {
    "shortName": "ADASController",
    "functionGroups": [
      {
        "shortName": "MachineFG",
        "states": ["Off", "Startup", "Driving", "Parking", "Shutdown"]
      },
      {
        "shortName": "SensorFG",
        "states": ["Inactive", "Active"]
      }
    ],
    "resourceGroups": [
      {
        "shortName": "adas_group",
        "cpuShares": 1024,
        "memoryLimit": "2G"
      }
    ],
    "networkManagement": {
      "nmHandleId": 0,
      "udpNmNetworkTimeout": 5000
    }
  }
}

Validation & Deployment

Manifests are validated before packaging using the AUTOSAR Adaptive manifest schema validator (provided by your stack vendor — Vector, EB, APEX.AI). Common validation errors:

serviceInterfaceRef path does not match any defined ServiceInterface in the ARXML
someIpServiceInstanceId collides with another service already registered on the machine
functionGroupStateMembership references a Function Group not declared in the Machine Manifest

🔍 Debug Tip

Run ara-manifest-validator --manifest application_manifest.json --schema /opt/autosar/schemas/ApplicationManifest.schema.json before packaging. Catching schema violations locally saves hours of runtime debugging.

Summary

The three-manifest architecture separates concerns cleanly: the Application Manifest governs process lifecycle, the Service Instance Manifest governs communication binding, and the Machine Manifest governs platform topology. Mastering these files is the prerequisite for all Adaptive deployment work.

🔬 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.