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Evaluating WCET in Hybrid Quantum-Classical Real-Time Systems for Autonomous Vehicles

qqubit365

2026-02-05

10 min read

How to evaluate WCET across classical control, LLM layers and quantum accelerators—practical roadmap after Vector’s RocqStat acquisition.

Hook: Why WCET Matters Now — and Why RocqStat’s Move to Vector Changes the Game

Developers and systems engineers building autonomous stacks face a new, acute problem: mixed workloads that combine deterministic control loops, high-latency large language model (LLM) decision layers, and quantum-accelerated kernels are breaking classical assumptions about predictability. You need provable timing bounds to certify safety (ISO 26262, UL 4600) and to integrate with fleet-level orchestration such as TMS platforms — yet established WCET toolchains were designed for purely classical embedded code. Vector Informatik’s January 2026 acquisition of RocqStat (StatInf’s timing-analysis team and tech) signals a significant, pragmatic shift: timing-analysis vendors are explicitly targeting the emerging reality of hybrid quantum-classical real-time systems. This article explains why that matters and gives a practical roadmap to evaluate WCET across mixed stacks for autonomous vehicles.

Executive Summary — The State of Play (2026)

Late 2025 and early 2026 saw two correlated trends that raise the bar for timing analysis in autonomy:

Toolchain consolidation: Vector’s purchase of RocqStat (announced January 16, 2026) aims to unify WCET and verification workflows inside VectorCAST, lowering friction between testing, static analysis and timing assurance.
System-level integration of autonomy services: Real deployments like the Aurora–McLeod TMS integration illustrate how autonomous vehicles are being brought into business processes; TMS scheduling and dispatch logic depends on reliable guarantees about task completion and availability.

Together, those trends make WCET an operational concern across engineering, product, and fleet ops — not just a research topic for embedded teams.

Why WCET is Harder in 2026: Three Technical Gaps

Mixing classical control, LLM decision layers, and quantum accelerators introduces three timing challenges:

Heterogeneous nondeterminism: Classical RTOS behavior is often predictable. LLMs introduce data-dependent and model-serving latency tails. Quantum accelerators introduce queueing, calibration and error-correction overhead that vary by backend and runtime configuration.
Cross-domain observability gaps: Existing WCET tools focus on CPU instruction paths or I/O. They lack built-in modelling for cloud-hosted model inference and quantum job lifecycles.
Certification friction: Safety standards require documented, auditable timing arguments. Probabilistic behaviors from ML and quantum tasks need new assurance patterns that can be integrated into existing verification artifacts.

Why Vector + RocqStat Matters for Hybrid Systems

The RocqStat capability is notable for three practical reasons:

Unified workflow — integrating timing estimation with unit/integration testing reduces handoffs and drift between development and verification teams.
Domain expertise continuity — acquiring the team (not just code) preserves know-how for advanced timing analysis methods that can extend to non-traditional task types.
Roadmap to toolchain support — the stated plan to fold RocqStat into VectorCAST hints at future first-class support for hybrid WCET workflows developers will need in 2026.

Practical implications for autonomy stacks

For system architects, the acquisition means an opportunity: start designing timing verification as a cross-domain pipeline now, expecting vendor toolchains to add primitives that represent LLM and quantum components as first-class timing actors.

A Minimal, Practical Methodology to Evaluate WCET in Quantum-Classical Real-Time Autonomy

The following methodology synthesizes best practices from static analysis, measurement-based methods, and modern verification thinking. Use it as a repeatable CI-capable workflow.

Inventory and classify tasks

Map every runnable unit to one of three categories: Deterministic classical control, Probabilistic inference/LLM, Quantum accelerator jobs. Capture invocation patterns, inputs, deadlines, and criticality (ASIL/mix-critical level) for each.
Apply the right timing technique per class
- Classical control: Use static WCET analysis (path analysis, abstract interpretation) where feasible. Tools like ILP-based WCET analyzers and compiler-assisted instrumentation remain gold standard.
- LLM inference: Use measurement-based statistical WCET (pWCET) with tail-risk modelling. Fit heavy-tailed distributions (Weibull/Generalized Pareto) to latency samples and bound tails with required confidence (e.g., 1e-6 probability of exceedance for high-ASIL tasks).
- Quantum jobs: Model three components: host-side prep/compile time, queue/dispatch time on the QPU service, and in-device execution including error-correction retries. Combine vendor SLAs, measured queue distributions, and conservative overhead from calibration/I/O.
Compose a system-level WCET

Use a task-graph model (DAG) and perform scheduling analysis (RM/EDF or time-triggered tables) to propagate component WCETs to end-to-end deadlines. For mixed-criticality tasks, apply temporal isolation strategies (partitioning, hypervisor time slices) to prevent LLM or quantum jitter from violating control deadlines.
Validate in hardware-in-the-loop (HIL) and emulation

Run worst-case workload scenarios in HIL and edge emulation. Capture long-tail events. Incorporate synthetic worst-case inputs for LLMs and stress patterns for the classical stack.
Integrate into CI and regression tests

Automate timing regression checks. Track metrics: 50/90/99/99.999 percentiles, max-observed latency, and monotonic increases. Fail builds on timing regressions that erode safety margins. This is an operational discipline closely aligned with modern SRE practices.
Document timing assurance artifacts for certification

Produce artifacts: WCET reports, input profiles, statistical models and validation runs. Link these artifacts to requirements in VectorCAST or your verification repository; forward-looking vendor tools (RocqStat+VectorCAST) are expected to streamline this linkage.

Concrete, Actionable Techniques — Code and Measurement Patterns

Below are practical patterns you can apply immediately. These snippets are not a replacement for a formal toolchain but are helpful for building a measurement baseline.

1) LLM latency harness (Python)

Wrap inference calls and build a tail-sampling harness. Collect thousands of samples under realistic loads and analyze tail behavior.

import time, statistics

def time_call(fn, *args, **kwargs):
    start = time.perf_counter()
    r = fn(*args, **kwargs)
    return (time.perf_counter() - start), r

samples = []
for _ in range(5000):
    latency, _ = time_call(call_inference_api, prompt=worst_case_prompt)
    samples.append(latency)

print('p99:', percentile(samples, 99))
print('max:', max(samples))

Action: fit the tail with a generalized Pareto distribution and select a bound consistent with your target probability of exceedance.

2) Quantum job timing harness (pseudo-code)

Measure host-side compile and round-trip queue times in the same manner. If you use a cloud QPU, capture vendor queue metrics and expand them by a conservative multiplicative factor for error-correction and retries.

# Pseudo-code
start = now()
job = qpu.compile(circuit)
compile_end = now()
submit_id = qpu.submit(job)
submit_end = now()
# poll until completion
wait_until(done)
end = now()

compile_time = compile_end - start
queue_time = submit_end - compile_end
exec_time = end - submit_end

Action: produce separate distributions for compile, queue and execution. Worst-case system planners should treat queue_time as non-negligible and often the dominant term for cloud QPUs.

Dealing with Probabilistic Components: Defensive Architecture Patterns

To meet strict real-time deadlines while using LLM/quantum services, adopt architectural patterns that convert probabilistic components into bounded behavioral kernels:

Time budget wrappers: Enforce hard deadlines with request timeouts and graceful degradation (fallback controllers or cached policies).
Speculative precomputation: Precompute multiple candidate decisions or quantum circuits for likely branches so the runtime can pick a precomputed result under tight deadlines.
Temporal isolation: Run LLM/quantum-heavy tasks on dedicated cores or host processors, with guaranteed bandwidth and time windows.
Health monitors and watchdogs: Monitor tail behavior and trigger safe-state transitions if latency envelopes grow unexpectedly.

Composing WCET Across Domains: A Worked Example

Consider an autonomous truck path-planning pipeline (TMS-integrated dispatch):

Sensor fusion (classical) — deterministic WCET from static analysis: 10ms
LLM-based route replanning (inference) — pWCET at 1e-6 exceedance: 1200ms
Quantum-accelerated optimization (QPU) — compose compile+queue+exec worst-case: 3000ms (cloud-backed) or 400ms (on-prem QPU)

System composition: if the path-planner is part of a hard-control loop with a 200ms deadline, you must avoid invoking the LLM/QPU synchronously. Options:

Run classical fallback planning synchronously; use LLM/QPU results only for mid-term replanning.
Use speculative precomputed alternatives so the synchronous loop never waits on an uncertain external service.

Verification, Standards, and the Road to Certification

Safety and verification teams will ask for auditable WCET evidence. Here’s how to make it credible:

Traceability: Map timing evidence to requirements and tests in your verification management tool (VectorCAST + future RocqStat integration helps here).
Statistical rigor: For pWCET, publish sampling method, sample size, confidence intervals and fitting methodology. Use conservative confidence levels appropriate to ASIL level.
Operational design domain (ODD) constraints: Document exactly when probabilistic services (LLM/QPU) are allowed to influence control decisions (e.g., not allowed in certain ODDs or only in advisory capacity).
Safety nets: Include deterministic watchdog paths and documented safe-states for timing violations.

"Timing safety is becoming a critical requirement" — Eric Barton, SVP Code Testing Tools, Vector (statement around Vector’s RocqStat acquisition).

2026 Trends to Watch (Late 2025 → 2026)

Toolchains converge: Expect WCET and verification ecosystems to absorb probabilistic/mixed-domain models as first-class artifacts — RocqStat’s integration into VectorCAST is an early indicator.
Edge QPUs and deterministic service modes: Late-2025 experiments showed early moves toward partitioned quantum co-processors that offer reserved time slots — this reduces queue variability when on-prem QPUs are available. For edge deployments consider pocket edge hosts and local co-processors.
Timing-aware ML runtimes: New inference runtimes expose predictable modes (quantization, batching limits) that lower tail risk at the cost of throughput.
Industry integration: Deployments like Aurora–McLeod’s TMS integration show business systems now depend on vehicle-side timing guarantees. Fleet planners will demand latency SLAs or predictable capacity reservation mechanisms.

Checklist: Immediate Steps for Engineering Teams (Actionable)

Inventory every component that participates in decision loops and assign a timing-analysis method.
Start large-scale latency sampling for LLMs and quantum backends under production-like loads.
Introduce timeouts and fallbacks for every probabilistic call path.
Model end-to-end task graphs and perform scheduling analysis with conservative bounds.
Integrate timing regression tests into CI and add alerts on metric drift.
Engage verification teams early — prepare artifacts expected by ISO 26262 and UL 4600 auditors.

Limitations and Open Research Questions

No single vendor tool solves the full hybrid WCET problem yet. Open questions include:

How to certify probabilistic ML components to ASIL-D-equivalent rigor?
How to model correlated failures between cloud services and vehicle networks?
What formal interfaces should exist between timing-analysis tools and vendor quantum runtimes?

These are active research areas. Expect vendor and academic progress in 2026 as more practitioners demand standardized artefacts.

Closing Thoughts — The Strategic Opportunity

Vector’s acquisition of RocqStat is more than a commercial transaction; it’s a signal that timing analysis is evolving to address hybrid, probabilistic workloads in safety-critical systems. If your organization is integrating LLMs or quantum accelerators into autonomy stacks — or if you’re operating at the interface between vehicle capabilities and fleet-level TMS — you must treat WCET as a cross-cutting systems problem. Start building measurement baselines today, adopt defensive architectural patterns, and fold timing assurance into your CI and verification pipelines.

Actionable Next Steps & Call to Action

Start a 4-week experiment plan this quarter:

Week 1 — Component inventory and test harness setup for LLM and QPU latency sampling.
Week 2 — Run baseline sampling (5k+ samples per endpoint) and fit tail models.
Week 3 — Compose system-level WCET and run HIL stress scenarios with synthetic worst-case inputs.
Week 4 — Integrate timing checks into CI and prepare a verification artifact bundle for auditors.

Want help operationalizing this? Join our weekly deep-dive for engineers working on hybrid autonomy stacks, or request a technical checklist tailored to your stack — we’ll map the steps into actionable tasks that fit your CI and verification pipelines. Start now: align engineering, verification and fleet ops around a concrete timing assurance plan before your next integration milestone.

Keywords: WCET, real-time, autonomy, RocqStat, quantum-classical, verification, timing analysis, safety, TMS

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