| Deliverable | Content | Standard Mapping |
|---|---|---|
| V&V strategy | Simulation + closed-track + public road plan | ISO 21448 Cl. 8 |
| Coverage targets | Scenario coverage % per TC; statistical confidence | Cl. 9 residual risk |
| Evidence tracker | Running total: scenarios executed vs required | Cl. 9 evidence accumulation |
Lab: SOTIF V&V Plan
Exercise 1: V&V Plan Document
YAMLsotif_vv_plan.yaml
# SOTIF V&V Plan for AEB v2.1
feature: AEB
standard: ISO 21448:2022
vv_phases:
simulation_known_scenarios:
tool: IPG CarMaker 12.0
scenarios: 2400 # all concrete scenarios from generator
target_pass_rate: 100% # all known scenarios pass
covers_tcs: [TC_001, TC_002, TC_003, TC_004, TC_005]
simulation_adversarial:
tool: CARLA 0.9.15 + custom perturbation
scenarios: 50000
target: "< 0.1% failure rate in full adversarial sweep"
purpose: "Q4 unknown scenario discovery"
closed_track_testing:
location: MIRA proving ground
scenarios: 150 # physical validation of simulation TCs
target_tcs: [TC_001, TC_002, TC_003] # weather-critical TCs
sensor_model_validation: true
public_road_testing:
total_km: 50000
regions: [DE, UK, FR, SE] # diverse weather/road
odd_coverage_target:
rain_hours: 200
night_hours: 500
urban_km: 20000
acceptance_criteria:
false_negative_rate:
target: "< 0.001 in light rain within ODD"
confidence: 0.95
required_scenarios: 2996
false_positive_rate:
target: "< 0.01 per 1000 km"
evidence: "50000 km public road testing"Summary
The SOTIF V&V plan is the master document that connects the hazard analysis outputs (triggering conditions, acceptance criteria) to the testing activities (simulation, track, public road) and the statistical evidence framework. The most important discipline is explicitly linking each V&V activity to the TCs it is designed to address and the acceptance criteria it must satisfy. A V&V plan that lists testing activities without this linkage cannot demonstrate completeness -- it is impossible to know whether a gap in the TC register has corresponding testing coverage. The YAML format used here enables automated completeness checking: a script can verify that every TC in the register appears in at least one V&V phase, and that every acceptance criterion has at least one evidence source.
🔬 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.