| Type | Description | Example |
|---|---|---|
| Performance-based | Numerical threshold on detection/false alarm rate | FN rate < 0.001 in light rain; FP rate < 0.01/1000km |
| Coverage-based | Minimum scenario coverage percentage | > 95% of ODD scenario space verified safe |
| Confidence-based | Statistical confidence on failure probability | 95% confidence that P(miss) < 0.001 in each TC category |
| Comparative | Performance relative to benchmark (human driver) | AEB false negative rate < 10% of human driver reaction miss rate |
| Process-based | Completion of required V&V activities | All TCs have executed simulation and track tests |
Acceptance Criteria Types in SOTIF
Acceptance Criteria Verification Script
"""Verify all SOTIF acceptance criteria are met before release."""
import json
from dataclasses import dataclass
from typing import List
@dataclass
class AcceptanceCriterion:
criterion_id: str
description: str
target: float
actual: float
pass_condition: str # "less_than" or "greater_than"
def passed(self) -> bool:
if self.pass_condition == "less_than":
return self.actual < self.target
return self.actual > self.target
def verify_release_criteria(criteria: List[AcceptanceCriterion]) -> bool:
all_pass = True
for c in criteria:
status = "PASS" if c.passed() else "FAIL"
print(f" [{status}] {c.criterion_id}: "
f"{c.actual:.4f} (target {c.pass_condition.replace("_"," ")} {c.target})")
if not c.passed(): all_pass = False
return all_pass
release_criteria = [
AcceptanceCriterion("AC_001", "FN rate light rain", 0.001, 0.0003, "less_than"),
AcceptanceCriterion("AC_002", "FP rate per 1000km", 0.01, 0.004, "less_than"),
AcceptanceCriterion("AC_003", "Scenario coverage", 0.95, 0.97, "greater_than"),
AcceptanceCriterion("AC_004", "Statistical conf.", 0.95, 0.963, "greater_than"),
]
result = verify_release_criteria(release_criteria)
print(f"\nRelease decision: {'APPROVED' if result else 'NOT APPROVED'}")Summary
Acceptance criteria definition is the bridge between SOTIF analysis and release decision. Well-defined criteria have three properties: they are quantitative (a number, not a judgment), they have a clear measurement method (which test results provide the actual value), and they have a clear traceability to the hazard analysis (each criterion addresses one or more identified hazardous events). Criteria that fail these properties -- "the system shall be safe in rain" without a quantitative definition of "safe" -- provide no protection against unsafe releases. The verification script pattern (programmatically checking all criteria against measured values and outputting a clear PASS/FAIL) turns the release decision from a potentially subjective judgment into an objective, reproducible check.
🔬 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.