Autonomous Driving Safety Data Pipeline

Transforms operational autonomous driving safety data from ongoing deployments into structured evidence for federal rulemaking, safety thresholds, standards development, and deployment policy decisions.

The Problem

“Autonomous Driving Safety Data Pipeline for Federal Rulemaking”

Organizations face these key challenges:

1

Safety data arrives in inconsistent schemas, formats, and reporting cadences across ADS operators

2

Critical evidence is buried in PDFs, narratives, video metadata, and engineering logs

3

Manual scenario labeling and severity assessment are slow and subjective

4

Cross-source deduplication of incidents and safety events is difficult

Impact When Solved

Cuts evidence preparation time for rulemaking and standards analysis from months to days or weeksImproves consistency of event taxonomy mapping across operators, jurisdictions, and reporting formatsEnables earlier detection of systemic safety patterns and edge-case failure modesProduces auditable evidence packages with source traceability for legal and policy review

The Shift

Before AI~85% Manual

Human Does

  • •Collect incident reports, disengagement logs, telematics extracts, and operator narratives from multiple sources
  • •Normalize inconsistent safety records into common spreadsheets or case files for comparison
  • •Review documents manually to label scenarios, assess severity, and identify possible duplicates
  • •Prepare statistical summaries, evidence briefs, and policy memos for rulemaking and standards analysis

Automation

    With AI~75% Automated

    Human Does

    • •Set evidence standards, review risk thresholds, and approve policy interpretations for regulatory use
    • •Validate high-impact or ambiguous cases flagged by the system for severity, causality, or deduplication review
    • •Decide on exemptions, standards positions, and deployment policy actions based on synthesized evidence

    AI Handles

    • •Ingest and organize structured and unstructured ADS safety data from operator submissions and public sources
    • •Extract comparable safety signals, map records to common taxonomies, and generate structured case summaries
    • •Classify scenario context, contributing factors, and event severity while scoring risk across operators
    • •Detect duplicate incidents, monitor for systemic safety patterns, and surface edge-case clusters for review

    Operating Intelligence

    How it works

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence88%
    ArchetypeRecommend & Decide
    Shape6-step converge
    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 shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    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 handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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