Simulating Driverless Fleet Events in CI/CD: Testing Your TMS with Autonomous Truck APIs

Stop deploying blind: simulate driverless fleets in CI/CD before you hit production

Pain point: Integrating a Transportation Management System (TMS) with autonomous truck APIs introduces complex, asynchronous lifecycle events (capacity acceptance, diversions, continuous telemetry). Deploying without realistic, repeatable tests risks missed tenders, routing mistakes, cost spikes and operator downtime. This article shows how to build CI pipelines and test harnesses that simulate autonomous truck lifecycle events so your TMS integration is validated end-to-end before production rollout.

Why API-level simulation matters in 2026

By 2026, enterprise TMS platforms are expected to support hybrid fleets that include human-driven and autonomous trucks. Industry integrations — like early links between autonomous providers and TMS vendors — have proven demand for programmatic tendering, dispatch and tracking. That trend moved testing from device- and hardware-centric simulation to API-level simulation: fast, deterministic, and CI-friendly.

Simulating at the API boundary lets you validate the contract, timing, and edge cases without needing a physical vehicle or full-scale simulator. You can run these simulations in ephemeral environments inside CI to validate behavior on every merge or release.

Core autonomous truck lifecycle events to simulate

Focus on the events your TMS will consume or emit. The most common—and most impactful—are:

• Capacity acceptance / Tendering: request for capacity and the autonomous fleet provider's acceptance or rejection.
• Dispatch & ETA updates: assignments, departure, ETA recalculations and confirmations.
• Diversion and reroute: unexpected route changes due to traffic, weather or maintenance.
• Telemetry streams: position, heading, speed, fuel/battery, diagnostics, sensor health.
• Heartbeat & liveness: connectivity checks, session lifetime, and failover signals.
• Exceptions & emergency events: emergency stop, hazard detection, or service shutdowns.

Designing a robust test harness architecture

Design the harness as a composable set of services you can run locally, in CI, or in an ephemeral Kubernetes namespace. Core components:

• Mock API server that implements the autonomous provider API contract for tendering, dispatch responses and control endpoints.
• Event generator for telemetry and lifecycle events that can reproduce deterministic and stochastic scenarios.
• Message broker (Kafka, NATS, MQTT) to simulate streaming telemetry and backpressure.
• Orchestrator for scenario scripts (Node/Python runner, or a state-machine engine) to sequence events, inject delays and faults.
• State store & assertion tools (Postgres/Redis) and contract testing (Pact or OpenAPI validations) to verify the TMS reactions.
• Observability: centralized logs, metrics, and traces (OpenTelemetry) to prove system behavior.

Architectural pattern

Keep the harness stateless where possible. Use containers for each component and a simple ingress to mimic the autonomous provider gateway. This lets CI spin up, run tests, and teardown predictably.

Choosing the right tools (2026 recommendations)

Tool selection depends on your stack. Recommended choices in 2026:

• Mock servers: WireMock for HTTP/RPC, Mountebank for multi-protocol stubs, and contract-first Pact for consumer-provider contracts.
• Streaming: Confluent Kafka for high-throughput telemetry, EMQX or HiveMQ for MQTT, and NATS for low-latency control messages.
• Load and chaos: k6 for telemetry load, Gremlin or Litmus for fault injection.
• Scenario runners: Node.js scripts (async/await) or Python asyncio for deterministic event sequences; Temporal or Zeebe if you need durable workflows.
• Ephemeral k8s: kind/k3d in CI for realistic cluster behavior; Pulumi/ArgoCD for reproducible manifests.

CI/CD pipelines: stage-by-stage

Design your pipeline with these stages. You can implement this in GitHub Actions, GitLab CI, Jenkins, or any modern CI platform.

1. Unit & contract tests: Verify request/response shapes using Pact/OpenAPI validators.
2. Smoke test with lightweight mocks: Start WireMock and run quick end-to-end tendering and acceptance flows.
3. Integration test in ephemeral infra: Bring up Kafka/MQTT and deploy the mock provider in an ephemeral k8s namespace. Run scenario scripts that exercise telemetry flows, diversions, and error conditions.
4. Performance & resilience tests: Generate realistic telemetry volumes and inject latency or dropped messages to validate your TMS backpressure handling and SLOs.
5. Security & contract gates: Validate TLS/mTLS, token expiry and signed payloads.
6. Teardown & artifacts: Collect logs, traces and test evidence; then teardown automatically.

Example GitHub Actions snippet (ephemeral k3d + tests)

name: autonomous-tms-integration

on: [push, pull_request]

jobs:
  integration:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout
        run: git checkout $GITHUB_SHA

      - name: Start k3d cluster
        run: k3d cluster create test-cluster --wait

      - name: Deploy mock provider and kafka
        run: |
          kubectl create namespace test-harness
          helm install mock-provider ./charts/mock-provider -n test-harness
          helm install kafka bitnami/kafka -n test-harness

      - name: Run contract tests
        run: npm run test:contracts

      - name: Run scenario tests
        run: npm run test:scenarios -- --env=test-harness

      - name: Collect artifacts
        run: kubectl logs -n test-harness --selector=app=mock-provider > mock-logs.txt

      - name: Teardown
        run: k3d cluster delete test-cluster
  
  

Implementing event simulation: concrete examples

Below are practical patterns and payloads you can use when building the generator and mock server.

Sample tender / capacity acceptance payloads

{ "tenderId": "TNDR-12345", "origin": {"lat": 41.8781, "lon": -87.6298}, "destination": {"lat": 34.0522, "lon": -118.2437}, "pickupWindow": {"start": "2026-01-20T08:00:00Z", "end": "2026-01-20T12:00:00Z"}, "load": {"weightKg": 22000} }

// Provider acceptance
{ "tenderId": "TNDR-12345", "accepted": true, "vehicleId": "AV-007", "dispatchId": "DSP-9876", "eta": "2026-01-20T09:45:00Z" }
  

Telemetry event (Kafka message) sample

{ "vehicleId": "AV-007", "timestamp": "2026-01-20T09:15:10Z", "position": {"lat": 39.0997, "lon": -94.5786}, "speedKph": 78.2, "heading": 270, "diagnostics": {"batteryPct": 86, "sensorsOk": true} }
  

Build a small generator that emits telemetry at configurable rates. Provide two modes: deterministic (replay from fixture) and stochastic (randomized jitter, network drop simulations).

Telemetry generator pseudocode (Node.js)

async function startTelemetry(vehicleId, topic, ratePerSec) {
  while (!stop) {
    const message = makeTelemetry(vehicleId)
    await kafkaProducer.send({ topic, messages: [{ value: JSON.stringify(message) }] })
    await sleep(1000 / ratePerSec)
  }
}
  

Validating behavior: assertions you must include

Tests must assert three dimensions: contract, timing, and state.

• Contract assertions: Use Pact/OpenAPI validators to assert every call your TMS makes matches the provider contract.
• Timing assertions: Validate that ETA updates are handled within expected windows and that stale telemetry is ignored after a cutoff.
• State assertions: Query the TMS database or API to confirm tender state transitions (e.g., Pending → Accepted → Dispatched → Delivered).

Observability and evidence in CI

Collect the following artifacts as CI evidence:

• WireMock/Mountebank request logs and mappings
• Kafka topic dumps for telemetry streams
• OpenTelemetry traces showing request paths and downstream latencies
• Database snapshots for state assertion
• Test reports (JUnit/Allure) and contract verification summaries

Security, compliance and production parity

Simulate the following security characteristics to get production-realistic tests:

• mTLS and token rotation: Validate certificate expiration and token refresh flows in CI.
• Signed telemetry: If telemetry is signed, include a verification step and test signature failure scenarios.
• Data minimization: Use synthetic data and scrub any PII; maintain replay only with synthetic or consented records.
• Compliance artifacts: Keep an audit trail for each test run that may be required by regulators or security reviews.

Advanced strategies: chaos, canaries and contract-first

Once the harness is in place, add these advanced layers:

• Chaos testing: Inject packet loss, delayed telemetry, or crash simulated vehicles to validate TMS tolerance.
• Contract-first: Publish provider schemas and generate mocks; make CI fail fast if a contract changes without a version bump.
• Canary in prod with synthetic traffic: With guarded controls and feature flags, run synthetic tenders against a subset of production to validate live behavior.
• Replay of historical incidents: Recreate past outages to validate that fixes prevent regressions.

Case study: what a rollout looks like

Hypothetical example from a mid-size carrier integrating autonomous capacity in 2026:

1. Developer builds Pact consumer tests for tendering flows.
2. CI runs a pipeline that spins up a k3d cluster with WireMock and Kafka; scenario tests run and validate acceptances and telemetries.
3. Performance tests push telemetry at 10x expected peak to verify ingestion and backpressure handling.
4. Chaos tests introduce intermittent broker failure to ensure the TMS buffers or re-requests updates.
5. After automated validation, a canary release sends real tenders to a single autonomous fleet provider for low-risk evaluation.

Result: fewer production incidents, faster onboarding of autonomous capacity, and measurable SLO improvements.

Practical checklist & quick wins you can implement this week

• Write Pact consumer tests for tender endpoints.
• Run a local WireMock with handshake responses and basic telemetry replay.
• Implement a simple telemetry generator that writes to a local Kafka topic.
• Add a CI job that spins up the mock stack, runs scenario scripts, and collects logs.
• Automate contract verification as a merge gate to prevent breaking provider APIs.

By shifting testing left and simulating realistic driverless fleet behavior in CI, teams reduce risk, shorten time-to-market, and increase confidence when enabling autonomous capacity in their TMS.

Final thoughts and next steps

In 2026, autonomous fleet integrations are no longer experimental; they are production realities requiring robust, automated testing. Your CI should simulate not just happy-path acceptance, but the full range of lifecycle events: diversions, dense telemetry streams, and failure modes. Build a modular harness, validate contracts continuously, and run resilience tests early and often.

Call to action: Start small: add contract tests and a lightweight mock provider in your CI this week. Need a jumpstart? We maintain a reference harness and CI templates tuned for TMS-autonomous integrations—get the sample repo, Helm charts and GitHub Actions workflows from our team to accelerate your rollout.

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