ADAS Scenario Regression Testing

Runs NCAP-focused scenario libraries to verify Lane Keeping Assist perception and steering support performance, automate regression testing, and compare behavior across validation cases for compliance verification.

The Problem

“ADAS Compliance Scenario Regression Testing for NCAP Lane Keeping Assist”

Organizations face these key challenges:

1

Manual execution and review of large scenario libraries is time-consuming

2

Perception and steering-support failures are hard to classify consistently

3

Comparing behavior across software versions requires custom scripts and analyst effort

4

NCAP-oriented evidence collection is fragmented across logs, video, and KPI files

Impact When Solved

Reduce manual scenario review time for LKA validation batchesIncrease repeatability of NCAP-aligned regression testing across software releasesDetect perception and steering-support regressions earlier in CI/SIL/HIL pipelinesGenerate structured compliance evidence and engineer-readable summaries

The Shift

Before AI~85% Manual

Human Does

  • •Configure and launch NCAP-focused LKA scenario batches for each software build
  • •Review simulation logs, video, and telemetry to identify perception or steering-support issues
  • •Calculate pass/fail KPIs across validation cases and compare results to prior releases
  • •Compile compliance evidence and regression findings into reports for engineering and safety review

Automation

    With AI~75% Automated

    Human Does

    • •Approve scenario scope, release gates, and NCAP-aligned evaluation criteria
    • •Review AI-flagged regressions and decide escalation, retest, or acceptance actions
    • •Validate compliance narratives and sign off on evidence used for engineering and safety decisions

    AI Handles

    • •Run predefined NCAP-focused scenario libraries automatically and monitor LKA regression results across builds
    • •Analyze scene, lane perception, and steering-support behavior to classify failures and detect anomalies
    • •Compare outcomes, KPI trends, and event patterns across validation cases and software versions
    • •Generate structured compliance evidence, pass/fail summaries, and engineer-readable test narratives

    Operating Intelligence

    How it works

    AI watches every signal continuously.

    Humans investigate what it flags.

    False positives train the next watch cycle.

    Confidence88%
    ArchetypeMonitor & Flag
    Shape6-step linear
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapelinear

    Step 1

    Observe

    Step 2

    Classify

    Step 3

    Route

    Step 4

    Exception Review

    Step 5

    Record

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Technologies

    Technologies commonly used in ADAS Scenario Regression Testing implementations:

    Lane detection modelsOther
    4 mentions
    NCAP compliance reportingOther
    4 mentions
    Steering assist controllerOther
    4 mentions
    Forward camera perceptionOther
    3 mentions
    Path planning / trajectory logicOther
    3 mentions

    Key Players

    Companies actively working on ADAS Scenario Regression Testing solutions:

    NissanTeslaToyota Motor Corporation

    Real-World Use Cases

    Lane Keeping Assist (LKA) perception and steering assistance workflow

    The car watches lane lines and helps keep the vehicle centered or nudges it back if it starts drifting out of its lane.

    Continuous scene perception with closed-loop steering assistancewidely deployed feature receiving stronger formal assessment under updated ncap.
    10.0

    Benchmark execution of scenario libraries for regression testing

    Teams can point the system at a folder of driving tests and have it run them all, like a test suite for self-driving software.

    Batch evaluationpractical and straightforward feature built into the runner, suitable for routine simulation test operations.
    10.0

    Free access to this report