Human Factors & Misuse Analysis - SOTIF (ISO 21448)

Human Factors in SOTIF Context

Why Human Factors Are Part of SOTIF

ISO 21448 explicitly includes human factors as a source of SOTIF hazards because ADAS systems create a human-machine collaboration where the allocation of authority and attention between driver and automation is a safety-critical design decision.

Human factors SOTIF hazards arise from:

Over-trust: Driver relies on ADAS beyond its ODD, assuming it will handle scenarios it cannot
Under-trust: Driver intervenes unnecessarily, causing more dangerous situation than ADAS would have created
Misunderstanding: Driver does not understand ODD boundaries; uses system outside its design envelope
Attention management: Driver reduces monitoring vigilance due to ADAS presence (automation complacency)

Misuse Taxonomy for ADAS

Misuse Type	Description	SOTIF Mitigation
ODD boundary misuse	Using AEB in fog where ODD excludes it	HMI ODD_EXIT warning; speed reduction suggestion
Automation complacency	Driver stops monitoring road because ACC is active	Hands-on-wheel detection; gaze monitoring; periodic alerts
Feature substitution	Using LKA as lane departure warning instead of lane keeping aid	Clear HMI feedback on system state and authority
Systematic disable	Driver disables AEB to avoid false alarms; leaves it off permanently	Investigate false alarm rate; auto-re-enable after parking
Over-reliance in edge case	Driver trusts ACC to handle construction zone queue	Proactive HMI: "Attention: construction zone -- ACC performance reduced"
Incorrect understanding	Driver believes Level 2 is Level 3 (hands-free)	SAE level clearly communicated in HMI; hands-on required indicator

User Understanding Test Protocol

Pythonuser_understanding_test.py

"""Protocol for testing driver understanding of AEB ODD."""

USER_UNDERSTANDING_QUESTIONS = [
    {
        "id": "UU_001",
        "scenario": "You are driving at 85 km/h on a motorway with AEB active.",
        "question": "Will AEB prevent a collision if a car stops suddenly ahead?",
        "correct_answer": "uncertain",
        "correct_explanation": "AEB operates up to 80 km/h; at 85 km/h, AEB is outside ODD",
        "common_wrong_answer": "yes",
        "sotif_risk": "Over-trust at ODD speed boundary"
    },
    {
        "id": "UU_002",
        "scenario": "Heavy rain has started. AEB warning light is amber.",
        "question": "What should you do?",
        "correct_answer": "increase following distance and drive more cautiously",
        "correct_explanation": "Amber warning = degraded performance; driver must compensate",
        "common_wrong_answer": "nothing; the system handles it",
        "sotif_risk": "Misunderstanding of HMI warning meaning"
    },
]

def evaluate_user_responses(responses: list) -> dict:
    correct = sum(1 for r in responses if r["correct"])
    return {
        "understanding_rate": correct / len(responses),
        "misuse_risks_identified": [
            q["sotif_risk"] for q, r in zip(
                USER_UNDERSTANDING_QUESTIONS, responses)
            if not r["correct"]
        ]
    }

Summary

Human factors analysis in SOTIF requires a different mindset from traditional safety engineering: instead of asking "what can break?", it asks "what will users misunderstand, misuse, or over-trust?". The user understanding test protocol directly addresses ISO 21448 Cl. 6.4 (human factors in hazard identification) by empirically measuring whether real users correctly understand ODD boundaries and HMI warnings. A user understanding rate below 80% for a safety-critical HMI warning is a SOTIF finding that requires a design change -- either a clearer warning, additional driver training content, or a more restrictive ODD definition that prevents the dangerous scenario entirely.

🔬 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.