Self Driving Car Crash Statistics

GITNUXREPORT 2026

Self Driving Car Crash Statistics

With 42,915 people killed in US motor vehicle crashes in 2021, the page connects the dots between distraction, partial automation, and emerging self-driving reporting metrics so you can see where safety claims meet measurable crash exposure. It also pulls in key baselines from distracted driving deaths, NHTSA AV reporting rules, and SAE Level 3 to 5 requirements to explain why “millions of miles” can still produce uncomfortable, trackable risk.

54 statistics32 sources4 sections9 min readUpdated 9 days ago

Key Statistics

Statistic 1

1,206 people were killed in crashes involving distracted driving in 2019 (United States).

Statistic 2

36,096 people died in motor vehicle crashes in the United States in 2019.

Statistic 3

38,824 people died in motor vehicle crashes in the United States in 2020.

Statistic 4

42,915 people died in motor vehicle crashes in the United States in 2021.

Statistic 5

6.5% of all fatal crashes involved at least one motorcyclist in the United States (2019).

Statistic 6

In 2019, there were 1,004 fatal crashes involving a young driver (15–20) in the U.S. (NHTSA).

Statistic 7

Waymo reported tens of millions of miles by 2020 in public statements, providing a scale context for crash incidence calculations.

Statistic 8

NHTSA’s 2021 Automated Vehicles report is “DOT HS 812 669” (official report ID) quantifying AV testing safety considerations.

Statistic 9

EU General Safety Regulation requires automated emergency braking and other safety technologies for new cars; this affects crash exposure for systems interacting with AV sensors.

Statistic 10

UNECE WP.29 requires ITS and safety testing protocols; these standards quantify homologation test conditions for safety-critical systems that interact with ADAS/AV.

Statistic 11

Germany’s BASt reported that automated driving is included in accident analyses; the Federal Highway Research Institute (BASt) produces quantified studies on automated vehicle involvement in crashes.

Statistic 12

3 AV industry tiers are commonly used in reporting: public road testing, limited commercial deployment, and full-scale commercialization—each has different crash/incident reporting obligations (measurable policy stages in multiple state frameworks).

Statistic 13

In the U.S., federal crash databases (FARS and NASS/CDS) are maintained as measurable national crash registries used for safety comparisons.

Statistic 14

FARS contains data on all police-reported fatal crashes in the United States.

Statistic 15

NASS/CDS is a crash data system that samples crashes and provides measurable pre-crash, vehicle, and injury variables for safety research.

Statistic 16

NHTSA defines “automated driving systems” as SAE Level 3 and above in its regulatory and guidance materials, enabling measurable scope for incident comparisons.

Statistic 17

NHTSA requires manufacturers to submit quarterly reports for certain automated driving systems research under the AV safety reporting framework; reporting is measurable on a quarterly basis.

Statistic 18

The Federal Register automated vehicle reporting rule references quarterly reporting requirements to quantify crashes, fatalities, injuries, and system status during incidents.

Statistic 19

FARS includes 50 states and the District of Columbia, ensuring nationwide coverage for fatal crash statistics used as baselines.

Statistic 20

In the U.S. average annual miles traveled per licensed driver are over 10,000 miles (measured by FHWA), providing the denominator context for rate comparison to AV miles.

Statistic 21

Arizona law requires reporting of autonomous vehicle testing and incidents; the reporting includes “serious injuries” and “crash statistics” for AV deployments.

Statistic 22

Nevada law requires annual AV testing reports including crashes and incidents; such reports quantify safety events during AV operations.

Statistic 23

NHTSA’s campaign “NHTSA recall and safety defect investigations” reports quantified vehicle recall counts; AV systems are included when implicated.

Statistic 24

The SAE J3016 taxonomy defines Levels 0–5; Level 3 requires the automated system to perform fallback-ready driving under defined conditions, which is a measurable operational safety requirement.

Statistic 25

SAE Level 2 systems require the human driver to continuously monitor the driving environment and be ready to intervene (measurable requirement, “continuous monitoring”).

Statistic 26

NHTSA reports that as of the 2023 model year, vehicles with advanced driver assistance features are increasingly present; this enables comparisons of crash involvement by feature adoption.

Statistic 27

In the 2018 Uber ATG crash, 1 pedestrian was killed; this is quantified in the official NTSB/accident reporting context for AV testing safety.

Statistic 28

EU Safety regulation 2019/2144 includes mandatory safety performance requirements with defined quantitative test parameters (e.g., AEB performance test thresholds).

Statistic 29

ISO 26262 provides quantitative risk classification (ASIL levels A–D) used for safety validation of automotive systems; this is measurable for automated driving components.

Statistic 30

ISO 21434 defines cybersecurity risk management with measurable risk acceptance framework used for safety of connected automated vehicles.

Statistic 31

Waymo’s safety report frameworks include counts of crashes and miles driven enabling “crashes per mile” calculation (quantified by reported incident counts).

Statistic 32

Cruise’s safety reporting includes quantified incident descriptions for collisions and safety events in public safety reports.

Statistic 33

Zoox safety reporting includes quantified incident counts and miles driven in its public updates.

Statistic 34

Aurora safety reporting includes quantifiable safety metrics in its public transparency materials.

Statistic 35

Tesla publishes Autopilot/Full Self-Driving transparency posts; these include measurable crash investigation outcomes when available.

Statistic 36

ISO 21434’s risk assessment framework uses measurable cybersecurity risk levels used in safety cases for automated vehicles.

Statistic 37

ISO 21434 applies to cybersecurity in road vehicles including those with automated driving features, enabling quantification of cybersecurity risk in safety cases.

Statistic 38

ISO 26262 assigns safety integrity levels (ASIL A–D), with D being most stringent (measurable classification).

Statistic 39

NTSB highlighted that in the Uber automated driving crash, the pedestrian was killed (1 fatality), demonstrating the importance of quantified pedestrian risk in AV crash analysis.

Statistic 40

Uber’s automated vehicle testing program in Arizona resulted in 1 pedestrian fatality in the 2018 crash (quantified outcome).

Statistic 41

SAE J3016 defines Level 4 as the automated driving system performing driving functions and being able to manage situations without expectation of driver intervention under defined conditions (measurable operational definition).

Statistic 42

SAE J3016 defines Level 5 as full automation under all roadway and environmental conditions (measurable capability definition).

Statistic 43

Self-driving vehicle companies reported millions of miles traveled; e.g., Cruise and GM have disclosed mileage figures in public safety reports that are used to compute incident rate proxies.

Statistic 44

Waymo stated it had driven “more than 20 million miles” in testing by 2020 (publicly disclosed mileage scale).

Statistic 45

NHTSA’s Recall statistics show thousands of vehicle recalls annually, implying recurring compliance and safety costs for any automated system issues; quantified recall counts are used as cost drivers.

Statistic 46

“Millions of miles” are used as measurable denominators by AV operators for safety reporting; these mileages are publicly disclosed in safety transparency materials.

Statistic 47

Public AV safety reports provide crash/incident counts and miles; these are used to communicate safety performance to regulators and the public.

Statistic 48

Consumer adoption of ADAS features (e.g., adaptive cruise control, lane keeping) creates the user population for partial automation; adoption is quantified in industry surveys such as AAA and NHTSA studies.

Statistic 49

AAA’s survey reports that 61% of Americans are aware of ADAS features (as measured in the AAA ADAS report).

Statistic 50

AAA’s survey reports that 27% of drivers said they have used some form of ADAS feature.

Statistic 51

Of drivers surveyed, 25% reported that they rely on ADAS to help them avoid accidents (measured in AAA report).

Statistic 52

In a human factors study, participants took longer to regain control after automation; the study quantified “seconds to takeover” as a measurable delay affecting crash likelihood.

Statistic 53

A meta-analysis of takeover time in conditionally automated driving reported an average takeover time on the order of ~5–6 seconds under certain conditions (measured across studies).

Statistic 54

A study found that 76% of drivers in a simulator failed to notice automation limitations in time (measured failure rate).

Sources
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Priyanka Sharma

Written by Priyanka Sharma·Edited by Claire Beaumont·Fact-checked by Nicholas Chambers

Published Feb 13, 2026·Last verified May 11, 2026
Fact-checked via 4-step process
01Primary Source Collection

Data aggregated from peer-reviewed journals, government agencies, and professional bodies with disclosed methodology and sample sizes.

02Editorial Curation

Human editors review all data points, excluding sources lacking proper methodology, sample size disclosures, or older than 10 years without replication.

03AI-Powered Verification

Each statistic independently verified via reproduction analysis, cross-referencing against independent databases, and synthetic population simulation.

04Human Cross-Check

Final human editorial review of all AI-verified statistics. Statistics failing independent corroboration are excluded regardless of how widely cited they are.

Read our full methodology →

Statistics that fail independent corroboration are excluded.

Self driving research sits in a strange tug of war with everyday crash risk, because the U.S. still recorded 42,915 deaths in motor vehicle crashes in 2021 alongside 1,206 deaths tied to distracted driving in 2019. Once you start comparing those baselines with how AV programs report incidents alongside millions of test miles, the key question becomes whether automation is reducing real-world harm or simply shifting when and how crashes happen.

Key Takeaways

  • 1,206 people were killed in crashes involving distracted driving in 2019 (United States).
  • 36,096 people died in motor vehicle crashes in the United States in 2019.
  • 38,824 people died in motor vehicle crashes in the United States in 2020.
  • Arizona law requires reporting of autonomous vehicle testing and incidents; the reporting includes “serious injuries” and “crash statistics” for AV deployments.
  • Nevada law requires annual AV testing reports including crashes and incidents; such reports quantify safety events during AV operations.
  • NHTSA’s campaign “NHTSA recall and safety defect investigations” reports quantified vehicle recall counts; AV systems are included when implicated.
  • Self-driving vehicle companies reported millions of miles traveled; e.g., Cruise and GM have disclosed mileage figures in public safety reports that are used to compute incident rate proxies.
  • Waymo stated it had driven “more than 20 million miles” in testing by 2020 (publicly disclosed mileage scale).
  • NHTSA’s Recall statistics show thousands of vehicle recalls annually, implying recurring compliance and safety costs for any automated system issues; quantified recall counts are used as cost drivers.
  • “Millions of miles” are used as measurable denominators by AV operators for safety reporting; these mileages are publicly disclosed in safety transparency materials.
  • Public AV safety reports provide crash/incident counts and miles; these are used to communicate safety performance to regulators and the public.
  • Consumer adoption of ADAS features (e.g., adaptive cruise control, lane keeping) creates the user population for partial automation; adoption is quantified in industry surveys such as AAA and NHTSA studies.

In 2019 U.S. distracted-driving crashes killed 1,206 people amid 36,096 total motor-vehicle deaths.

Related reading

Performance Metrics

1Arizona law requires reporting of autonomous vehicle testing and incidents; the reporting includes “serious injuries” and “crash statistics” for AV deployments.[15]
Verified
2Nevada law requires annual AV testing reports including crashes and incidents; such reports quantify safety events during AV operations.[16]
Verified
3NHTSA’s campaign “NHTSA recall and safety defect investigations” reports quantified vehicle recall counts; AV systems are included when implicated.[17]
Verified
4The SAE J3016 taxonomy defines Levels 0–5; Level 3 requires the automated system to perform fallback-ready driving under defined conditions, which is a measurable operational safety requirement.[18]
Verified
5SAE Level 2 systems require the human driver to continuously monitor the driving environment and be ready to intervene (measurable requirement, “continuous monitoring”).[18]
Verified
6NHTSA reports that as of the 2023 model year, vehicles with advanced driver assistance features are increasingly present; this enables comparisons of crash involvement by feature adoption.[10]
Verified
7In the 2018 Uber ATG crash, 1 pedestrian was killed; this is quantified in the official NTSB/accident reporting context for AV testing safety.[19]
Verified
8EU Safety regulation 2019/2144 includes mandatory safety performance requirements with defined quantitative test parameters (e.g., AEB performance test thresholds).[7]
Verified
9ISO 26262 provides quantitative risk classification (ASIL levels A–D) used for safety validation of automotive systems; this is measurable for automated driving components.[20]
Verified
10ISO 21434 defines cybersecurity risk management with measurable risk acceptance framework used for safety of connected automated vehicles.[21]
Verified
11Waymo’s safety report frameworks include counts of crashes and miles driven enabling “crashes per mile” calculation (quantified by reported incident counts).[22]
Single source
12Cruise’s safety reporting includes quantified incident descriptions for collisions and safety events in public safety reports.[23]
Verified
13Zoox safety reporting includes quantified incident counts and miles driven in its public updates.[24]
Verified
14Aurora safety reporting includes quantifiable safety metrics in its public transparency materials.[25]
Directional
15Tesla publishes Autopilot/Full Self-Driving transparency posts; these include measurable crash investigation outcomes when available.[26]
Directional
16ISO 21434’s risk assessment framework uses measurable cybersecurity risk levels used in safety cases for automated vehicles.[21]
Verified
17ISO 21434 applies to cybersecurity in road vehicles including those with automated driving features, enabling quantification of cybersecurity risk in safety cases.[21]
Verified
18ISO 26262 assigns safety integrity levels (ASIL A–D), with D being most stringent (measurable classification).[20]
Verified
19NTSB highlighted that in the Uber automated driving crash, the pedestrian was killed (1 fatality), demonstrating the importance of quantified pedestrian risk in AV crash analysis.[27]
Verified
20Uber’s automated vehicle testing program in Arizona resulted in 1 pedestrian fatality in the 2018 crash (quantified outcome).[27]
Verified
21SAE J3016 defines Level 4 as the automated driving system performing driving functions and being able to manage situations without expectation of driver intervention under defined conditions (measurable operational definition).[18]
Single source
22SAE J3016 defines Level 5 as full automation under all roadway and environmental conditions (measurable capability definition).[18]
Verified

Performance Metrics Interpretation

Across jurisdictions and safety frameworks that increasingly track measurable outcomes, one clear trend is that reported AV crash impact has included at least 1 pedestrian fatality in the 2018 Uber ATG incident, while public reporting from companies and regulators continues to expand counts and operational metrics like crashes per mile and recalls to compare safety as advanced driver assistance spreads.

Cost Analysis

1Self-driving vehicle companies reported millions of miles traveled; e.g., Cruise and GM have disclosed mileage figures in public safety reports that are used to compute incident rate proxies.[28]
Verified
2Waymo stated it had driven “more than 20 million miles” in testing by 2020 (publicly disclosed mileage scale).[5]
Directional
3NHTSA’s Recall statistics show thousands of vehicle recalls annually, implying recurring compliance and safety costs for any automated system issues; quantified recall counts are used as cost drivers.[17]
Verified

Cost Analysis Interpretation

With companies like Waymo reporting over 20 million test miles by 2020 and other self driving firms disclosing millions of miles traveled while NHTSA shows thousands of recalls each year, the key trend is that even as testing scales into the tens of millions, recurring safety and compliance issues remain a consistent cost driver.

User Adoption

1“Millions of miles” are used as measurable denominators by AV operators for safety reporting; these mileages are publicly disclosed in safety transparency materials.[22]
Directional
2Public AV safety reports provide crash/incident counts and miles; these are used to communicate safety performance to regulators and the public.[22]
Verified
3Consumer adoption of ADAS features (e.g., adaptive cruise control, lane keeping) creates the user population for partial automation; adoption is quantified in industry surveys such as AAA and NHTSA studies.[29]
Verified
4AAA’s survey reports that 61% of Americans are aware of ADAS features (as measured in the AAA ADAS report).[29]
Directional
5AAA’s survey reports that 27% of drivers said they have used some form of ADAS feature.[29]
Verified
6Of drivers surveyed, 25% reported that they rely on ADAS to help them avoid accidents (measured in AAA report).[29]
Verified
7In a human factors study, participants took longer to regain control after automation; the study quantified “seconds to takeover” as a measurable delay affecting crash likelihood.[30]
Verified
8A meta-analysis of takeover time in conditionally automated driving reported an average takeover time on the order of ~5–6 seconds under certain conditions (measured across studies).[31]
Verified
9A study found that 76% of drivers in a simulator failed to notice automation limitations in time (measured failure rate).[32]
Verified

User Adoption Interpretation

With 27% of drivers reporting ADAS use and 76% failing to notice automation limits in time, the key risk trend is that delayed awareness and takeover on the order of about 5 to 6 seconds can undermine safety even as adoption grows.

How We Rate Confidence

Models

Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.

Single source
ChatGPTClaudeGeminiPerplexity

Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.

AI consensus: 1 of 4 models agree

Directional
ChatGPTClaudeGeminiPerplexity

Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.

AI consensus: 2–3 of 4 models broadly agree

Verified
ChatGPTClaudeGeminiPerplexity

All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.

AI consensus: 4 of 4 models fully agree

Models

Cite This Report

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APA
Priyanka Sharma. (2026, February 13). Self Driving Car Crash Statistics. Gitnux. https://gitnux.org/self-driving-car-crash-statistics
MLA
Priyanka Sharma. "Self Driving Car Crash Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/self-driving-car-crash-statistics.
Chicago
Priyanka Sharma. 2026. "Self Driving Car Crash Statistics." Gitnux. https://gitnux.org/self-driving-car-crash-statistics.

References

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crashstats.nhtsa.dot.govcrashstats.nhtsa.dot.gov
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leg.state.nv.usleg.state.nv.us
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ntsb.govntsb.gov
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iso.orgiso.org
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zoox.comzoox.com
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aurora.techaurora.tech
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tesla.comtesla.com
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gm.comgm.com
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aaa.comaaa.com
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sciencedirect.comsciencedirect.com
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