An engineer-ready compliance framework mapping EU AI Act obligations onto ADAS development workflows — from concept gate to homologation. Free to use. Premium templates available.
The EU AI Act (Regulation 2024/1689) is the world's first comprehensive AI regulatory framework. For automotive ADAS developers, it introduces mandatory conformity obligations for systems classified as high-risk AI.
ADAS AI is listed in Annex III. Providers must register systems in the EU database and complete conformity assessments before market deployment.
Annex IV mandates comprehensive Technical Files covering system design, training data, validation methodology, and performance metrics — version-controlled.
Article 72 requires continuous monitoring plans. Providers must collect and analyze real-world performance data and report serious incidents to authorities.
Article 14 requires high-risk AI to enable human oversight. ADAS systems must include override capabilities and interpretable outputs for driver intervention.
Risk management system — documented, iterative process covering the entire AI lifecycle.
Training, validation, and testing data governance. Data quality, bias identification, labeling traceability.
Technical documentation — must be updated continuously and available to notified bodies upon request.
Logging requirements: automatic event logs, tamper-proof, sufficient to trace system behavior in deployment.
Transparency and provision of information. Instructions for use must be clear for downstream deployers.
Accuracy, robustness, cybersecurity. Systems must be resilient to adversarial inputs and edge cases.
Quality management system (QMS) integration. Must cover design, development, testing, and post-market phases.
Source: Estimated from published regulatory readiness surveys, 2024–2025. Values are indicative.
Map your ADAS AI components to EU AI Act risk levels. High-risk systems require full Article 9–17 compliance. Lower-risk components still need minimal documentation.
| ADAS Component | AI Type | Risk Level | Key Obligations | Article Refs |
|---|---|---|---|---|
| Pedestrian / Object Detection Camera + LiDAR fusion perception |
CNN / Transformer | 🔴 Critical High-Risk | Full Technical File, FMEA integration, bias testing across demographics & lighting conditions | Art. 9–15 Ann. IV |
| Driver Monitoring System Drowsiness, distraction, incapacitation detection |
Facial / Gaze AI | 🔴 Critical High-Risk | Biometric data governance (Art. 10), demographic bias testing, override logging, GDPR alignment | Art. 9–15 GDPR |
| Automated Emergency Braking Collision prediction and braking actuation |
Sensor Fusion + RL | 🔴 Critical High-Risk | Scenario coverage evidence, edge case logging, fail-safe documentation, robustness testing | Art. 15 Ann. IV §2 |
| Lane Keeping Assist Lane detection and steering torque control |
Segmentation CNN | 🟠 High-Risk | Training data diversity, validation evidence, performance metrics across ODD boundaries | Art. 10–12 |
| Adaptive Cruise Control Speed regulation via object distance AI |
Radar ML Model | 🟠 High-Risk | ODD specification, failure mode documentation, logging of override events | Art. 9 Art. 12 |
| Traffic Sign Recognition Sign classification and speed limit assist |
Image Classifier | 🟡 Medium-Risk | Dataset documentation, accuracy benchmarks, regional coverage evidence | Art. 10–11 |
| Parking Assistance AI Low-speed automated maneuver |
Spatial Planning | 🟡 Medium-Risk | Operational design domain documentation, user instruction clarity | Art. 13 |
| Predictive Maintenance AI In-vehicle sensor anomaly detection |
Time-series ML | 🟢 Low-Risk | Minimal documentation; transparency notice if driver-facing outputs | Art. 50 |
EU AI Act Risk Pyramid — ADAS Components
Art. 9–17 obligations apply in full to Critical & High-Risk tiers · Annex III § 3 Transport
Article 11 + Annex IV define what must be documented. For ADAS teams, this means machine-readable, version-controlled artifacts — not static PDFs.
This is the gap most ADAS teams discover late. The left column reflects typical engineering artifacts; the right column shows what a notified body or authority will look for.
Plus: Art. 12 logging schema, CI/CD validator YAML, dataset card template, RACI export formats.
{
"event_id": "uuid-v4",
"session_id": "string",
"timestamp_utc": "ISO8601",
"model_version": "semver",
"decision_type": "enum",
"confidence": "float",
"override_flag": "boolean",
"override_cause": "enum|null"
}
Includes:
Embed compliance into your ADAS development stages. Stages 01-02 Free 🔒 Stages 03-05 Premium
ODD coverage evidence, robustness testing (Art. 15), human oversight verification (Art. 14), V&V evidence linking to Technical File.
Technical File completeness review, conformity assessment (Art. 43), EU database registration (Art. 49), PMM plan activation, Instructions for Use (Art. 13).
Compliance infrastructure runs in your existing toolchain. These categories cover logging, traceability, ALM integration, and validation.
Vendor-neutral observability framework for structured event logging. Supports trace context for AI decision chains.
High-throughput event streaming with schema enforcement. Kafka's immutable log provides tamper-evident event history.
Lightweight Art. 12-aligned event schema: timestamp, session_id, model_version, decision_type, confidence, override_flag.
Open-source ML experiment tracking. Logs parameters, metrics, artifacts, and model registry with full run provenance.
Dataset versioning with hash-based integrity. Tracks dataset changes alongside model code — critical for Art. 10 data lineage.
Comprehensive experiment tracking with model registry, dataset cards, and audit logging for compliance evidence packages.
Industry-standard automotive ALM. Create custom Art. 9–15 requirement types, link to test evidence, export Technical File sections.
Requirements traceability with live impact analysis. Map EU AI Act articles to system requirements and track coverage status.
JSON Schema validators for Technical File artifacts running in GitHub Actions / GitLab CI. Block non-compliant merges automatically.
EU AI Act compliance is a team sport. The matrix below assigns Responsible, Accountable, Consulted, and Informed roles across key compliance activities.
| Compliance Activity | ML Engineer | Data Engineer | Systems Engineer | QA / Test Engineer | Functional Safety Lead | Product Owner | Legal / Compliance |
|---|---|---|---|---|---|---|---|
| Risk Classification (Art. 6) | C | – | R | C | A | I | C |
| Risk Management System (Art. 9) | R | C | R | C | A | I | I |
| Data Governance (Art. 10) | R | A | I | C | C | I | C |
| Technical File (Ann. IV) | R | R | R | R | A | C | I |
| Logging Implementation (Art. 12) | R | R | C | C | A | I | – |
| Human Oversight Design (Art. 14) | C | – | R | R | A | I | – |
| Robustness Testing (Art. 15) | R | C | C | A | C | I | – |
| QMS Integration (Art. 17) | I | I | C | R | A | C | C |
| Conformity Assessment (Art. 43) | C | I | C | R | A | R | R |
| Post-Market Monitoring (Art. 72) | R | R | C | C | A | I | I |
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