DC Autonomous Vehicle Delay Dashboard

Estimated preventable deaths since Jan 1, 2023
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Based on crash-reduction research and a 1.5% effective VMT-share assumption (Kusano et al.)
Total DC road deaths
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Since Jan 1, 2023

The Delay

A timeline of DC's autonomous vehicle regulatory failure

2020
DC passes Autonomous Vehicle Testing Program law
Mandates DDOT complete a safety study by late 2022
Late 2022
DDOT misses legally mandated deadline
Safety study not completed; regulatory limbo begins
Jan 2023
Delay clock starts
AV deployment blocked without completed study
April 2024
Waymo begins testing in DC with safety drivers
Limited testing only — no public ride-hail service
69 road deaths since delay began
July 2025
McDuffie introduces B26-0323
Bill to enable AV deployment in DC
119 road deaths since delay began
Dec 2025
Allen refuses to advance bill
Councilmember Allen (Ward 6) blocks enabling legislation
129 road deaths since delay began
Apr 2026
Still in regulatory limbo
No AV ride-hail service permitted in DC
139 road deaths since delay began

The Data

Waymo's safety record applied to DC

DC Traffic Fatalities (2019–2025)

2019
27
2020
37
2021
40
2022
35
2023
52
2024
52
2025
25

* 2025: NSC estimate; 2026: projected from YTD

Waymo Safety Reductions (Peer-Reviewed)

Injury crash reduction -85%
Pedestrian injury reduction -92%
Intersection crash reduction -96%
Miles analyzed 7.1 million

Applied to DC (annual, based on 2024)

Preventable deaths/year (full deployment) 44
Preventable deaths/year (1.5% effective VMT share) 0.7

1.5% effective VMT share = DC ride-hail VMT (~6.9%, Fehr & Peers) × observed Waymo rideshare penetration in SF (~22%).

Source: Kusano et al. (2024) — 85% fewer any-injury-reported crashes over 7.1M autonomous miles

Source: Kusano et al. (2025) — 85% fewer suspected serious injury+ crashes, 79% fewer any-injury-reported crashes over 56.7M miles

Source: Swiss Re / Waymo (2024) — Waymo vehicles had zero bodily injury claims vs. human baseline

Ride-Hail Safety Comparison

Incident rates across ride-hail providers and national baseline

Provider Metric Rate
Waymo Serious injuries / million miles 0.02
Uber Accidents / million miles 0.45
Lyft Accidents / million miles 0.38
National avg. Fatalities / 100M VMT 1.35

Methodologies differ across these sources. Waymo data uses police-reported incidents matched to location baselines; Uber/Lyft figures are self-reported. Direct comparison should be interpreted with caution.

Sources: Kusano et al., Traffic Injury Prevention (2025), 56.7M miles · Uber Safety Report (2021–2022) · Lyft Safety Report (2021–2022) · NHTSA FARS

Meanwhile, Elsewhere

Months from first Waymo testing to commercial ride-hail service

2023
2024
2025
2026
Los Angeles
12 mo
Austin
15 mo
Atlanta
5 mo
Washington
24+ mo
Testing → launched Testing → still waiting

Methodology & Sources

Full transparency on data, assumptions, and limitations

How the counter works

The live counter estimates preventable deaths by interpolating DC's cumulative traffic fatalities since January 1, 2023 (when the DDOT safety study was already overdue) and applying Waymo's peer-reviewed safety reduction factor.

DC fatality data comes from MPD / Vision Zero crash reports: 2023 (52 deaths), 2024 (52 deaths), 2025 (25, NSC estimate), 2026 (32, projected from 8 YTD). Note: NHTSA FARS figures for DC differ slightly due to the federal 30-day rule and different inclusion criteria. Deaths are interpolated linearly within each year to produce a real-time estimate.

Estimate methodology

The headline figure applies an 85% any-injury-reported crash reduction to 1.5% of vehicle miles traveled. The 85% comes from Kusano et al. (2024), which analyzed 7.1M rider-only miles. A subsequent 2025 study covering 56.7M miles found a 79% reduction in any-injury-reported crashes (95% CI: 71–85%) and 85% fewer suspected serious injury+ crashes (95% CI: 39–99%).

The headline estimate uses an illustrative 1.5% effective VMT-share assumption. This is derived by multiplying DC ride-hail's share of city-core vehicle miles traveled (about 6.9%, Fehr & Peers, 2019) by a benchmark Waymo share of the rideshare market (about 22%, based on San Francisco at end of 2024; data from YipitData).

Key assumptions & limitations

  • The 85% reduction comes from Kusano et al.'s analysis of 7.1 million autonomous miles. Real-world deployment at scale could differ.
  • Fatality reductions are extrapolated from injury crash data. Fatality-specific reductions may be higher or lower than the overall injury crash reduction.
  • The 1.5% effective VMT share is derived from DC ride-hail VMT (~6.9%, Fehr & Peers 2019) multiplied by observed Waymo rideshare penetration in San Francisco (~22% by end of 2024, YipitData).
  • These figures represent potential lives saved, not certainties. AV technology continues to improve, and real-world results will depend on deployment specifics.
  • DC's road conditions, driver behavior, and infrastructure differ from cities where Waymo data was collected (primarily Phoenix and San Francisco).

Data sources

Disclaimer: This dashboard presents estimates based on publicly available data and peer-reviewed research. It is intended to illustrate the potential human cost of regulatory delay, not to predict exact outcomes. The authors are advocates for autonomous vehicle deployment and present this data in good faith to inform public discourse. All sources are linked for independent verification.