Hands-On: SOTIF Scope Definition - SOTIF (ISO 21448)

Lab: SOTIF Scope Definition for AEB

Deliverable	Content	Format
ODD definition	Speed range, weather, road type, lighting conditions	YAML structured definition
Triggering condition list	10+ TCs across all taxonomy categories	Structured TC register
Scenario matrix	4-quadrant classification of 20+ scenarios	Spreadsheet / Python tracker

Exercise 1: ODD Definition for AEB

YAMLaeb_odd.yaml

# AEB Operational Design Domain (ODD) per ISO 21448
feature: Automatic Emergency Braking (AEB)
version: "2.1"

odd:
  speed:
    min_kmh: 10
    max_kmh: 80
    note: "Above 80 km/h: driver warning only, no autonomous braking"

  road_types:
    included: [highway, urban, rural_paved]
    excluded: [unpaved, construction_unmarked, private]

  weather:
    rain:
      max_intensity: light  # up to 2 mm/h
      note: "Heavy rain: HMI warning; AEB performance not guaranteed"
    fog:
      min_visibility_m: 100
    snow:
      included: false  # AEB deactivated when snow detected on sensor
    sun_angle_deg:
      note: "Direct sun glare: degraded mode, HMI warning"

  lighting:
    included: [daylight, well_lit_urban_night]
    excluded: [unlit_rural_night]
    note: "Unlit night: AEB sensor range limited; HMI warning"

  target_types:
    vehicles:    true
    pedestrians: true
    cyclists:    true
    animals:     false  # not in AEB scope
    stationary:  true
    moving:      true

odd_boundary_behaviour:
  action: "Display ODD_EXIT warning; reduce AEB confidence threshold"
  driver_notification: true
  autonomous_braking: false  # outside ODD: advisory only

Exercise 2: Triggering Condition Register

Pythontc_register.py

"""AEB triggering condition register -- 12 TCs."""

TC_REGISTER = [
  {"id":"TC_001","cat":"Weather",      "desc":"Rain > 2mm/h limits camera range < 30m",         "severity":3,"exposure":"high"},
  {"id":"TC_002","cat":"Weather",      "desc":"Fog visibility < 100m before sensor detects",     "severity":3,"exposure":"med"},
  {"id":"TC_003","cat":"Lighting",     "desc":"Sun glare causing camera saturation",              "severity":3,"exposure":"high"},
  {"id":"TC_004","cat":"Lighting",     "desc":"Unlit tunnel transition: sudden darkness",         "severity":2,"exposure":"med"},
  {"id":"TC_005","cat":"Target",       "desc":"White/silver vehicle vs bright sky, low contrast", "severity":3,"exposure":"med"},
  {"id":"TC_006","cat":"Target",       "desc":"Partially occluded pedestrian (parked car edge)",  "severity":3,"exposure":"high"},
  {"id":"TC_007","cat":"Target",       "desc":"Crossing pedestrian >3 m/s (child running)",      "severity":3,"exposure":"med"},
  {"id":"TC_008","cat":"Road",         "desc":"Construction zone: sudden stationary queue",       "severity":3,"exposure":"med"},
  {"id":"TC_009","cat":"Road",         "desc":"Speed limit change: 100->30 in short distance",   "severity":2,"exposure":"med"},
  {"id":"TC_010","cat":"Human",        "desc":"Driver over-reliance: not monitoring at 79 km/h", "severity":2,"exposure":"high"},
  {"id":"TC_011","cat":"Human",        "desc":"Driver disables AEB via menu before parking",     "severity":1,"exposure":"low"},
  {"id":"TC_012","cat":"Operational",  "desc":"Approach velocity > 25 m/s (>90 km/h) at ODD edge","severity":3,"exposure":"low"},
]

high_sev = [t for t in TC_REGISTER if t["severity"] == 3]
print(f"Total TCs: {len(TC_REGISTER)}")
print(f"Severity S3 (fatal): {len(high_sev)}")

Summary

The SOTIF scope definition lab produces the two foundational documents for all subsequent SOTIF analysis: the ODD definition (which sets the boundary of what the system must handle) and the triggering condition register (which identifies the specific conditions that can cause hazardous behaviour within that ODD). The ODD YAML format is particularly useful because it is machine-readable: automated test case generation tools can read the ODD boundaries and generate test scenarios at the ODD boundary conditions automatically, ensuring that the most critical transitions (inside ODD to outside ODD) are always tested.

🔬 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.