Safety Impact

Although comparing crash rates boils down to 4 simple counts – crashes and miles for Automated Driving System (ADS) and a benchmark – there are many decisions about the study design and data sources used that can affect the outcome. Safety impact research has been a well-used tool in the vehicle safety research literature, dating back to safety advances like electronic stability control and automated emergency braking. ADS which are responsible for the entire dynamic driving task present some unique challenges, and as a result the RAVE Checklist was published as a consensus of research best practices for ADS safety impact research. The checklist, which is being developed into an international standard, lays out the best practices for conducting safety impact studies of ADS like presented on the Safety Impact Data Hub. The research that underpins the safety impact data hub is designed to comply with the RAVE Checklist (see the online appendix of Kusano et al., 2025, for a conformance assessment of the methods against the RAVE checklist requirements).

Waymo’s safety impact research is based on reporting required by the National Highway Traffic Safety Administration (NHTSA) Standing General Order (SGO). All Automated Driving System (ADS) operators (a technical term for automated vehicle operators like Waymo), including Waymo, must adhere to the SGO and report all crashes meeting the requirements within the specified reporting windows. NHTSA has the authority to investigate and take corrective action if they believe there are reporting inconsistencies with ADS operator SGO reports. The SGO reporting requirements include crashes with minor damage, which is a lower reporting threshold (more minor crashes are included) than traditional police-reported and insurance crash databases. All crashes where any injury is alleged to have occurred or any airbag is deployed, which are the outcomes the safety impact data hub results focus on, must be reported as part of the SGO. Therefore, given the stringent reporting requirements and operational policies of Waymo’s fleet, it is highly unlikely that any crashes resulting in the outcomes reported on the data hub occurred and are not included. For reference, NHTSA reports (Blincoe, et al., 2023) that underreporting for human-driven vehicle crashes is 69.7% of property damage crashes and 31.9% of injury crashes. Waymo’s reporting is for all known crashes that are detected by a highly capable sensor suite, a more complete reporting. 

Because Waymo is using police-reported data to derive benchmarks, only crashes where the Waymo vehicle is transported and contacted during the crash are included in the comparison to the benchmark. In police-reported data, vehicles that are not contacted during a collision sequence are not included as vehicles in the crash data. Therefore, comparing Waymo crashes reported in the SGO where there was no contact to the Waymo vehicle (but may be reported as part of the SGO due to alleged contribution to the crash) would overcount the Waymo crash rate relative to the crash rate. Similarly, the Waymo vehicle is sometimes parked in a valid parking space waiting to serve customers in the future. The ADS software is active, but the vehicle is in park and in a valid parking space (either a marked space or within 18 inches of a curb for on-street parking). In police-report data, parked vehicles like these are also not included in the vehicle count (parked vehicles are considered fixed objects). 

Aligning the Automated Driving System (ADS) and human crash data is one of the most important dimensions of doing a fair apples-to-apples comparison, and an important step to aligning data is coming up with a consistent definition for a “crash.” Waymo’s Safety Impact research uses past safety evaluation research as a starting point to pick crash outcomes that can be best identified in both ADS and human data sources. The most prevalent and reliable human crash data source are police report databases. Not all human crashes are reported to police, especially minor ones. More serious crashes that result in air bag deployments or injury (either serious injuries or worse, or any level of injury) are more relevant to assessing safety than those that result in small amounts of property damage.

Even though we believe serious injury or worse, airbag deployment, and any-injury-reported outcomes are more relevant to assessing safety than those that result in small amounts of property damage, we still track and report these minor collision rates compared to benchmarks available in the downloads section of the data hub website (for example, any property damage or injury and police-reported).

The Waymo safety impact research uses several approaches to align the driving conditions between the human benchmark and Waymo driving: (a) using human data from the counties where Waymo operates and (b) a location-based, dynamic benchmark adjustment. Driving is different in different cities, and not all roads or driving conditions are equally risky. To capture the local crash risk, Waymo’s safety impact research uses state-maintained crash and vehicle miles traveled (VMT) data sources restricted to the counties where Waymo currently operates. Even within a county, human crash rates differ based on what part of the county you drive in. Generally, more dense areas of cities have higher crash rates than less densely populated areas. To capture this effect, Waymo’s safety impact results use a dynamic benchmark adjustment that weights the human benchmark proportionally to the miles the Waymo service drives in each area (see Kusano et al., 2025 and Chen et al., 2025, for details). By comparing Waymo driving to benchmark driving from the same locations, many of the driving condition effects are implicitly accounted for. Our research has shown that crash rates vary substantially by geographic location, which is why we suggest against using a national average benchmark to compare with Waymo’s driving.

Better aligning the benchmark crash rates to the Waymo driving environment through local crash data and the dynamic adjustment accounts for many but not all possible factors that may affect crash risk. For example, the current cities Waymo operates in do not have appreciable snow fall, and as a result neither the Waymo nor the human benchmark data include this type of inclement weather. Chen et al. (2025) found that time of day affects crash rates (crash rates late at night are generally higher than during the day). The bottleneck for accounting for more factors when aligning the benchmark and Waymo data is often a lack of data for the human driving exposure. For example, the VMT data used to do the dynamic benchmark is provided as an annual average, so it can’t be used to adjust for time of day. We are investigating other data sources that could help provide human data to additionally align the benchmark and Waymo data.

The results on the safety impact data hub compare Waymo’s crash performance to the current human driving fleet from the areas where Waymo operates using best practices to align the Waymo and human crash data. This comparison answers the research question, “what is the effect of Waymo’s driving on the status quo.” This type of research question is the most basic question researchers ask when a new vehicle technology is being developed and deployed (for example, automated emergency braking, electronic stability control). This type of status quo comparison demonstrates the potential of a vehicle technology to improve traffic safety.

Some of Waymo’s other research has investigated comparison to other populations. For example, in prior research and our prospective safety determination methodologies examining our collision avoidance performance, we readily compare the Waymo Driver’s performance against a “non-impaired with eyes on the conflict (NIEON)” driver. There are methodological challenges with creating a comparable crash rate version of this benchmark, because the exact amount of VMT for a “NIEON” like driver is not readily available to quantify benchmarks, primarily due to the fact that human drivers are not always in a NIEON state when driving.  In other work from Swiss Re in collaboration with Waymo (in peer review), Waymo’s third party claims rates have been compared to human drivers driving the latest-generation vehicles. This represents another, higher performing subset of human-driven vehicles because latest-generation vehicles typically have improved safety features.

Another potentially enlightening comparison could be with other driving populations like taxis or human ride-hailing. Today, there are no publicly available (and therefore independently verifiable) data sources for quantifying crashes and VMT for these special populations across a wide range of outcomes like is done for general police report and public VMT databases.  Another benchmark that would represent a furtherance expectation could be non-impaired driver benchmark. While this can be a valuable comparison, it does not provide an assessment of reduction on the status quo crash rate. Similar to the special population rates, it’s difficult to produce a local estimate of both the number of impaired crashes and impaired VMT. These are challenging but valuable areas of further research as new data sources become available.

Injury outcomes can be measured in a variety of different ways. We did not want our analysis to be overly focused on the occupants of the Waymo vehicle, because this could undercount Waymo’s safety impact in being involved in crashes that injure people outside of Waymo’s vehicle. So, we chose a crash-level outcome that looks at the maximum sustained injury of any person involved across the entire crash sequence. A maximum, crash-level injury score is common practice in automotive safety research, and is actually often provided directly in police reports as an entry field.

The research shows that the Waymo Driver is safer than the overall human driver population in the same geographical areas where it operates, measured by the number of crashes of a given outcome per vehicle mile traveled. The research has focused on comparing the Waymo Driver’s safety performance to the entire collection of human-driven vehicles within the same geographical area. The human crash rate can be thought of as the “status quo” of driving for that area. This comparison is used in safety impact analysis to determine how effective the introduction of Waymo’s  technology is compared to the status quo. 

The overall human driver crash rate is commonplace, and made possible due to the longstanding data reporting practices almost universally present throughout the United States and much of the world.  There is a strong historical precedent for examining year-over-year trends and systemic challenges facing entire geographic driver populations. While crash data offers some insight into various performing subsets (e.g., factors like vehicle type or driver intoxication), the corresponding VMT data typically lacks such fine-grained granularity for detailed breakdown. For example, to be able to compare crash rates for factors like driver intoxication, you would need to know the VMT for drivers with intoxication or estimate it. Significantly less effort has been dedicated to isolating specific subsets of drivers for comprehensive crash risk analysis.

While a statistically significant reduction represents a safety benefit (i.e., there are fewer crashes), a claim regarding “safe enough” is made through Waymo’s Safety Framework and Safety Case before the release of an ADS configuration. The goal of safety impact is not to establish what is or is not a reasonable level of safety for an automated driving system. Waymo uses its Safety Framework to determine safety readiness according to approval guidelines for a given software release candidate. Additionally, independent analysis of the appropriateness of such a process is conducted through the Safety Case. A safety case is a formal way to explain how an ADS developer determines that its system is safe enough to be deployed on public roads without a human driver. The safety case includes evidence to formally determine absence of unreasonable risk. It involves an explanation of the system, the methodologies and metrics used to validate it and the actual results of validation tests. Conversely, the retrospective evidence provided by the Safety Impact Data Hub serves a post-deployment validation role for the Safety Framework and Safety Case. This cycle of continuously building confidence in the Safety Framework and Safety Case processes also builds confidence that the process will yield similar safety impact results as Waymo expands into new areas. 

Most of the safety impact research uses crashes from all Rider-Only (RO) miles accumulated to-date. Waymo’s driving miles over time have greatly increased, such that the more recent data makes up a larger proportion of the Waymo driving miles than the older miles. Similar to the FAQ on “why aren’t the comparisons of Waymo Rider-Only driving to the benchmark crash rates divided into more categories?”, dividing the driving miles into smaller portions reduces the statistical power of the analysis, which is a common limitation noted in other safety critical fields. 

Grouping multiple software releases, or even data from different manufacturers, is commonplace in the safety impact research done on past safety systems. For example, the research from the Insurance Institute for Highway Safety and the PARTS consortium often looks at a technology like Automated Emergency Braking or Lane Departure Prevention and groups several manufacturers together to determine the overall impact of a technology. Similarly, the Waymo safety impact research shows the overall impact of the Waymo Driver. As more driving mileage is accumulated, there is an opportunity to investigate Waymo’s safety impact on smaller time periods.

Waymo’s safety impact research strives to answer the research question “what is Waymo’s safety impact over current human-driven vehicle crash rates?” (the status quo). A slightly different, and equally as important, question is “how is Waymo confident that new software and hardware releases are safe?” To answer this second question, Waymo has developed a Safety Framework and Safety Case Approach. In short, they evaluate Waymo’s performance on every new candidate configuration using a collection of methodologies that span vehicle architecture, driving behavior, and operational layers against acceptance criteria.

Waymo has published a wide range of benchmarks, including fatal crash involvement, that will be used in future evaluations. Currently, there is not enough Waymo VMT to detect statistical significance in the areas that we drive and therefore we do not separate out that fatalities-only category in our reporting. The Waymo Driver is also inherently designed to mitigate or eliminate the top causes of fatal collisions according to the latest NHTSA data: speeding, impaired and distracted driving, and unbelted passengers. The “serious injury or worse” category includes both seriously injured and fatalities. All other crash outcome categories also include fatalities. 

Waymo’s approach has been to (a) proactively publish benchmarks, methodology, and intended analytical lenses, (b) perform evaluations on those established benchmarks when previously completed power analyses indicate that significance may be detected, and (c) publish the findings on our data hub and in scientific publications. 

As has been the case for many safety innovations in the history of vehicle safety, there are other ways to determine the potential of a technology before it is widely deployed and miles are accumulated. For example, our research that reconstructed fatal crashes involving human drivers in Chandler, AZ found the Waymo Driver avoided 100% of simulated, fatal crashes when it was the initiator, and 82% of collisions even when it was the responder. This type of study, when paired with Waymo’s safety readiness determination process, shows that the Waymo Driver has a tremendous potential to reduce serious and fatal injuries.

In automotive safety research, injuries at or above a certain level are commonly studied. In the case of our analysis, “serious injury or worse” includes both suspected serious injuries (denoted as “A”-level or incapacitating injuries on the KABCO scale used on police reports in the US) and fatal injuries (denoted as “K”-level injuries on the KABCO scale). Waymo has published benchmarks that include “K”-level crashes, any fatality, as its own category. This outcome is not currently reported as part of the Safety Impact Data Hub, but we intend to add this outcome at a later date.  

If we looked at “serious injury” (only “A”-level injuries) by itself, we could potentially introduce a type of exclusion bias. For example, if a treatment were to create only fatal outcomes and very few suspected serious injury outcomes, it could erroneously lead to the conclusion the treatment is much safer than it is, because the “fatal” injuries were not being counted. By adding in the “at or above” stipulation, we avoid this potential fallacy. 

The Waymo safety impact research investigates the difference in crash rates between Waymo vehicles and human drivers that drive in comparable areas. Remote Assistance enables the Waymo Driver to contact a human agent for additional information to help contextualize its surroundings in certain challenging or uncommon situations. Remote Assistance has been part of the design of the Waymo Driver since the beginning, and it is indeed part of the reason Waymo has been able to scale its operations safely. Waymo’s Remote Assistance program has received an independent, third-party audit that shows it conforms to industry best practices in this area and is indeed autonomous according to industry best practices (1, 2). 

The Waymo Driver is currently driving millions of miles per week. The Waymo Driver has the cumulative experience of hundreds of human lifetimes of driving, when taking into account miles driven on the road and in simulation. At this scale, many of the challenging situations, like pedestrians suddenly appearing from behind a parked car or another vehicle running a red light, happen regularly. If the Waymo Driver could not handle many of the challenging situations that humans handle throughout their lifetime, then Waymo’s crash rates would not be so much lower than human drivers.

Waymo uses its Safety Framework to determine safety readiness according to approval guidelines for a given software release candidate. Additionally, independent analysis of the appropriateness of such a process is conducted through the Safety Case. A safety case is a formal way to explain how an ADS developer determines that its system is safe enough to be deployed on public roads without a human driver. The safety case includes evidence to formally determine absence of unreasonable risk. It involves an explanation of the system, the methodologies and metrics used to validate it and the actual results of validation tests.

The results of Waymo’s safety impact research show that compared to the current status quo of human driven vehicles, Waymo has fewer injury-causing crashes per vehicle mile traveled. Part of the benefit is that there is sometimes no one in the Waymo vehicle (e.g., while the vehicle is traveling to or from a depot to charge or between serving riders). It is important to note that the metrics examined by Waymo’s safety impact research considers an injury to any person involved in the crash sequence, whether or not the person is inside a Waymo vehicle. This includes human vulnerable road users, such as pedestrians and cyclists, or the occupants of other vehicles involved in a crash. Therefore, even if there is some benefit from the Waymo vehicle being unoccupied sometimes, it’s unlikely this unoccupied benefit alone explains Waymo’s large reduction in injury-causing crashes (the vehicle could be unoccupied all the time and still get in crashes that may injure people outside the vehicle). Other outcomes, like the airbag deployment metrics, are not affected by the Waymo vehicle occupancy. The Waymo vehicle airbags will fire regardless of occupancy of the Waymo vehicle. The magnitude of the airbag reduction compared to the benchmark is similar to the injury-causing reduction, increasing the confidence that the observed benefits are not highly dependent on Waymo vehicle occupancy.

Aligning the Waymo and human crashes and driving is one of the most important factors for making a fair “apples-to-apples” comparison of crash rates (see question 1.1 for more details on alignment). 

We have focused our early efforts on three components that we believe are informative for safety evaluation. 

• Collision severity - establishing multiple levels ranging from police-reported to fatality.  

• Crash type (shown below) - the typology we selected was based on prior research from NHTSA highlighting the most challenging driving scenarios. 

• Road type - We break down crash rates by surface streets and freeways. We currently only have limited miles on freeways, so we focus on surface streets, only. But, we plan to differentiate between the two road type groupings in our future publications when the VMT enables a statistical comparison. 

We are actively working to expand the analytical lenses we apply to evaluating the Waymo Driver. However, we are generally limited by the available human crash data. Waymo is actively relying on publicly available crash and mileage data. This data has limited information about the specifics of each individual human crash. Conversely, Waymo’s data is rich with information due to our constant monitoring of VMT and our ability to capture each crash with our wide array of sensors. To expand our analysis, we are continuously investigating the usage of new data sources with more fine-grained information, and looking toward the broader community for analyses and data that can help support the research.

All crashes involving Waymo vehicles operating in Rider-Only (RO) configuration are included in the safety impact analysis. Therefore, the collision risk of the Waymo vehicle stopping on a roadway and another vehicle subsequently colliding with the stopped Waymo vehicle is included in the safety impact. These types of stopped vehicle crashes are also included in the human benchmark.

This analysis included all collisions, regardless of the party at fault and Waymo’s responsibility. Moreover, the question of fault in causing or contributing to a collision is a legal determination. That said, the recent peer reviewed study led by Swiss Re showed that over 3.8 million miles, the Waymo Driver reduced the frequency of property damage insurance claims by 76% and completely eliminated bodily injury claims compared to human drivers.

Citation:

• Di Lillo, L., Gode, T., Zhou, X., Atzei, M., Chen, R., & Victor, T. (2024). Comparative safety performance of autonomous-and human drivers: A real-world case study of the Waymo Driver. Heliyon, 10(14). https://doi.org/10.1016/j.heliyon.2024.e34379

A subsequent study using insurance claims data that is currently under peer review found the Waymo RO service had similarly large reductions compared to humans over 25 million miles driven. In addition to an overall human benchmark, this new study also introduces a “new model year” vehicle benchmark. The newest vehicles (defined as model years 2018 to 2021) had lower property damage and bodily injury claims rates compared to the overall population. Waymo had an 88% reduction in property damage claims, 92% in bodily injury claims compared to the overall population, and an 86% reduction in property damage claims and 90% in bodily injury claims. All of these differences were statistically significant.

Citation:

• Di Lillo, L., Gode, T., Zhou, X., Chen, R., & Victor, T. (2024). Do Autonomous Vehicles Outperform Latest-Generation Human-Driven Vehicles? A Comparison to Waymo’s Auto Liability Insurance Claims at 25 Million Miles.

Today, Waymo’s service is comparable to human ride-hailing services. The data show Waymo prevents serious injury or worse, airbag deployment, and any-injury-reported by more than 80%. In order for the introduction of Waymo to lead to a net increase in crashes, Waymo would need to increase overall VMT by over 80%, which does not seem like a realistic assumption. There are many studies that show that overall VMT and vehicles on the road can be greatly reduced with the introduction of shared autonomous vehicles (for example 1, 2, 3, 4, 5).

Traffic safety is a public health issue and the 2030 Agenda for Sustainable Development has set an ambitious target of reducing road traffic deaths and injuries worldwide by 50% by 2030. A study by the RAND Corporation modeled Automated Driving System (ADS) deployments under several assumptions, including a system that had a crash rate that is only marginally lower than current humans or waiting years to deploy a system with a much lower crash rate than humans. The findings were that more harm could be prevented by deploying earlier. 

Waymo has a safety framework and safety case approach that has a top level goal of deploying a Rider-Only (RO) system that has an absence of unreasonable risk (AUR). This safety case goal is accomplished by decomposing the possible hazards of the system by several dimensions, setting acceptance criteria, and assessing both the claims and evidence before deploying. This process is designed to ensure the Waymo Driver is acceptably safe before deploying.

We do not need to choose one technology or policy initiative to combat the traffic safety crisis. Automated vehicles, like the Waymo Driver, are one of many tools available for improving traffic safety. Waymo is committed to the safe system approach and Vision Zero, which through multiple improvements for safer roads, safer speeds, safer vehicles, safer road users, and safer post-crash care. Many improvements to safety (like investments in safer roads, setting safe speed limits, enforcing existing traffic laws, improving seat belt compliance, reducing impaired driving, to name a few) will also make riding in a Waymo safer. Waymo, like much of the industry, is a privately funded company. We as a society can support the expansion of automated vehicles without taking away from other safety improvements.

Automated vehicles pose a unique opportunity compared to other safety technologies because of the relatively large safety impact over human driving. For example, Automated Emergency Braking reduces rear-end striking crashes (which are only about a quarter of all crashes) by approximately 50%. In comparison, the Waymo Driver reduces crashes resulting in any-injury-reported by approximately 80% across all crash modes, including intersection and VRU crashes where current active safety technologies are not yet significantly reducing crashes.

The Safe System approach, based on the global Vision Zero movement, is a systematic method that aims to eliminate serious and fatal injuries in the road transportation system. Waymo’s Automated Vehicles provide a valuable tool in the Safe System toolkit because they are designed to follow principles of Vision Zero. Waymo requires seat belt use by all occupants. Waymo is designed to follow the speed limit and uses vehicles with the latest passive safety features. 

In this analysis, we use publicly available data — specifically, Waymo’s crash reports submitted under NHTSA’s Standing General Order (SGO) — to enable other researchers to replicate the results. The data displayed on this webpage undergoes consistent updates aligned with the NHTSA SGO reporting timelines.

In addition to new data being published, we may update the methodology used to do comparisons between the Waymo RO (Rider Only) service and human benchmarks. The best practices in retrospective safety impact is an evolving science. When we do make changes in methodology, we will communicate those changes and their effects on the results and interpretation of the data. For more details, see the release notes documents available in the downloads section.

This information is important to analyze and understand collisions and is not available in the NHTSA SGO. Data after June 2025 does not have zip code, because this field was removed from the NHTSA SGO reporting form (see SGO amendment 3). The zip code field was added back to the SGO reporting form in September 2025. SGO events reported after September again have zip code in the data download file.