Robotaxi Disclosure in SEC Filings (edgar-autonomy)

A primary-source dataset of what six companies — Tesla, Alphabet (Waymo), GM (Cruise), Amazon (Zoox), Uber, and Lyft — actually say about robotaxis and autonomy in their SEC filings, measured over time. It separates narrative (how much a company talks about autonomy, and in what tone) from commitment (the hard operational numbers it will put in a legally-liable filing). Every figure traces to the exact sentence in the source document.

Built entirely from public SEC EDGAR filings, with no paid APIs. Companion to edgar-tok and tcontorno/edgar-ma-deals.

• Interactive dashboard: see the GitHub repo below
• Code & full documentation: https://github.com/tcontorno6/edgar-autonomy

Coverage

Companies (CIK-verified)	Tesla, Alphabet/Waymo, Uber, Lyft, GM/Cruise, Amazon/Zoox
Forms	10-K, 10-Q, 8-K (with EX-99 press-release exhibits); amendments excluded
Period	filings dated 2019-01-02 to 2026-09-29 (fiscal periods through Q2 2026)
Corpus	762 filings / 1,137 documents / 9.27M words
Source	SEC EDGAR only (public domain)

Waymo, Cruise, and Zoox are not SEC registrants; everything about them comes from their parents' filings, which is part of the finding.

Files

File	Rows	What it is
attention.csv	372	Autonomy mention rate per 1,000 words and share of filings, per company × quarter × group (robotaxi-service vs. autonomy-tech).
quantification_summary.csv	6	Per company: count of distinct operational autonomy numbers in filing text, their subjects, and what the company notably never quantified.
quantification_numbers.csv	29	Every operational number found, with its parsed value, unit, qualifiers (cumulative vs. per-period, driverless vs. safety-driver, etc.), confidence, and the verified source sentence and SEC URL.
stance_vs_baseline.csv	712	Loughran-McDonald tone of autonomy passages per company × quarter × filing section, reported relative to the non-autonomy text of the same section.
gm_winddown.csv	31	GM's Cruise/robotaxi tone by quarter, plus wind-down and expansion vocabulary share.

Full column dictionaries are in the GitHub repo.

How it was built

1. Corpus — EDGAR 10-K/10-Q/8-K; CIKs verified; EX-99 exhibits included; 0 fetch or clean errors.
2. Attention — curated keyword set in two groups; word-boundary and case-sensitive matching (so "Cruise" the product is separated from "cruise control"); table and corporate-transaction mentions excluded.
3. Quantification — regex plus a local relevance model; every number must match its source sentence at stored offsets (0 citation failures). Figures that appear only as chart images are rejected, not guessed.
4. Stance — Loughran-McDonald (2011) lexicons, scored on the autonomy sentence only and reported relative to a same-section baseline, so risk-factor boilerplate does not masquerade as negativity.

Each phase was validated on a held-out sample with the rules frozen before drawing it.

Selected findings

• Across 1,137 documents, the six companies disclosed only 29 distinct operational autonomy numbers in filing text. The only driverless operating figure in seven years is Alphabet's one disclosure of Waymo's weekly ride count.
• Talk runs opposite to action: the companies that mention autonomy most (Uber, Lyft, GM) back it with the fewest numbers.
• Controlled for section, five of six companies write about autonomy more positively than their own baseline; the raw "everyone is negative" reading is a risk-factor artifact.
• GM's robotaxi tone turns negative in Q4 2023, a year before GM publicly pulled Cruise funding in December 2024; wind-down vocabulary rises from 0% of passages to ~80%.

Validation (held-out)

Phase	Check	Result
Mentions	wrong-sense false positives / strict precision	1.1% / 84%
Numbers	value correct / value + claim correct	100% / 95%
Stance	label vs. human read	80%

Limitations

• Loughran-McDonald counts accounting words like gain and benefit regardless of subject, so positive-tone levels should be read with care.
• Earnings-call transcripts, where most operational numbers are spoken, are out of scope; this measures what reaches the legal record.
• Alphabet (1 number) and Amazon (4) are descriptive, not trends. One reviewer set all labels.

Source and citation

Underlying filings are U.S. SEC EDGAR documents (public domain). Sentiment lexicons are from Loughran, T. and McDonald, B. (2011), When Is a Liability Not a Liability?, Journal of Finance — used to build the stance measures and not redistributed in this dataset.

Dataset by Tyler Contorno, 2026. This is a research dataset of disclosure language, not investment advice.