In the United States, 42,915 people died in motor vehicle crashes in 2021, up from 36,096 deaths in 2019. Distracted driving accounted for 1,206 deaths in 2019. AV programs now publish incidents and millions of test miles, so the analysis turns to whether automation lowers overall harm or changes crash timing and exposure.
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
01 · Category
Industry Trends20 stats
01
1,206 people were killed in crashes involving distracted driving in 2019 (United States).
02
36,096 people died in motor vehicle crashes in the United States in 2019.
03
38,824 people died in motor vehicle crashes in the United States in 2020.
04
42,915 people died in motor vehicle crashes in the United States in 2021.
05
6.5% of all fatal crashes involved at least one motorcyclist in the United States (2019).
06
In 2019, there were 1,004 fatal crashes involving a young driver (15–20) in the U.S. (NHTSA).
07
Waymo reported tens of millions of miles by 2020 in public statements, providing a scale context for crash incidence calculations.
08
NHTSA’s 2021 Automated Vehicles report is “DOT HS 812 669” (official report ID) quantifying AV testing safety considerations.
09
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.
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.
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.
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).
13
In the U.S., federal crash databases (FARS and NASS/CDS) are maintained as measurable national crash registries used for safety comparisons.
14
FARS contains data on all police-reported fatal crashes in the United States.
15
NASS/CDS is a crash data system that samples crashes and provides measurable pre-crash, vehicle, and injury variables for safety research.
16
NHTSA defines “automated driving systems” as SAE Level 3 and above in its regulatory and guidance materials, enabling measurable scope for incident comparisons.
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.
18
The Federal Register automated vehicle reporting rule references quarterly reporting requirements to quantify crashes, fatalities, injuries, and system status during incidents.
19
FARS includes 50 states and the District of Columbia, ensuring nationwide coverage for fatal crash statistics used as baselines.
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.
Interpretation
Industry Trends Interpretation
In 2019 in the United States, 1,206 people were killed in crashes involving distracted driving, out of 36,096 total motor vehicle crash deaths, showing that while distracted driving is a major contributor, overall fatalities still rose from 36,096 in 2019 to 42,915 by 2021.
02 · Category
Performance Metrics22 stats
01
Arizona law requires reporting of autonomous vehicle testing and incidents; the reporting includes “serious injuries” and “crash statistics” for AV deployments.
02
Nevada law requires annual AV testing reports including crashes and incidents; such reports quantify safety events during AV operations.
03
NHTSA’s campaign “NHTSA recall and safety defect investigations” reports quantified vehicle recall counts; AV systems are included when implicated.
04
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.
05
SAE Level 2 systems require the human driver to continuously monitor the driving environment and be ready to intervene (measurable requirement, “continuous monitoring”).
06
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.
07
In the 2018 Uber ATG crash, 1 pedestrian was killed; this is quantified in the official NTSB/accident reporting context for AV testing safety.
08
EU Safety regulation 2019/2144 includes mandatory safety performance requirements with defined quantitative test parameters (e.g., AEB performance test thresholds).
09
ISO 26262 provides quantitative risk classification (ASIL levels A–D) used for safety validation of automotive systems; this is measurable for automated driving components.
10
ISO 21434 defines cybersecurity risk management with measurable risk acceptance framework used for safety of connected automated vehicles.
11
Waymo’s safety report frameworks include counts of crashes and miles driven enabling “crashes per mile” calculation (quantified by reported incident counts).
12
Cruise’s safety reporting includes quantified incident descriptions for collisions and safety events in public safety reports.
13
Zoox safety reporting includes quantified incident counts and miles driven in its public updates.
14
Aurora safety reporting includes quantifiable safety metrics in its public transparency materials.
15
Tesla publishes Autopilot/Full Self-Driving transparency posts; these include measurable crash investigation outcomes when available.
16
ISO 21434’s risk assessment framework uses measurable cybersecurity risk levels used in safety cases for automated vehicles.
17
ISO 21434 applies to cybersecurity in road vehicles including those with automated driving features, enabling quantification of cybersecurity risk in safety cases.
18
ISO 26262 assigns safety integrity levels (ASIL A–D), with D being most stringent (measurable classification).
19
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.
20
Uber’s automated vehicle testing program in Arizona resulted in 1 pedestrian fatality in the 2018 crash (quantified outcome).
21
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).
22
SAE J3016 defines Level 5 as full automation under all roadway and environmental conditions (measurable capability definition).
Interpretation
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.
More related reading
03 · Category
Cost Analysis3 stats
01
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.
02
Waymo stated it had driven “more than 20 million miles” in testing by 2020 (publicly disclosed mileage scale).
03
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.
Interpretation
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.
04 · Category
User Adoption9 stats
01
“Millions of miles” are used as measurable denominators by AV operators for safety reporting; these mileages are publicly disclosed in safety transparency materials.
02
Public AV safety reports provide crash/incident counts and miles; these are used to communicate safety performance to regulators and the public.
03
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.
04
AAA’s survey reports that 61% of Americans are aware of ADAS features (as measured in the AAA ADAS report).
05
AAA’s survey reports that 27% of drivers said they have used some form of ADAS feature.
06
Of drivers surveyed, 25% reported that they rely on ADAS to help them avoid accidents (measured in AAA report).
07
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.
08
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).
09
A study found that 76% of drivers in a simulator failed to notice automation limitations in time (measured failure rate).
Interpretation
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.
Reference
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.
Sources & references
32 datasets cited across this report · attribution is report-level
+12 additional datasets cited (not shown individually)