How safe are self-driving cars compared with human drivers?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   Safety of Autonomous Vehicles

   Journal of Advanced Transportation (Wang et al.)Published Sep 15, 2020Checked Oct 3, 2026

   “In addition, 128 accidents in 2014–2018 are studied, and about 63% of the total accidents are caused in autonomous mode. A small fraction of the total accidents (∼6%) is directly related to the AVs, while 94% of the accidents are passively initiated by the other parties, including pedestrians, cyclists, motorcycles, and conventional vehicles. These safety risks identified during on-road testing, represented by disengagements and actual accidents, indicate that the passive accidents which are caused by other road users are the majority. The capability of AVs to alert and avoid safety risks caused by the other parties and to make safe decisions to prevent possible fatal accidents would significantly improve the safety of AVs. Practical applications. This literature review summarizes the safety-related issues for AVs by theoretical analysis of the AV systems and statistical investigation of the disengagement and accident reports for on-road testing, and the findings will help inform future research efforts for AV developments.”

2. 2

   Safer Than the Average Human Driver (Who is Less Safe than Me)? Examining a Popular Safety Benchmark for Self-Driving Cars

   Research paper (Nees)Published Mar 23, 2019Checked Oct 3, 2026

   “Although the level of safety required before drivers will accept self-driving cars is not clear, the criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation. This criterion actually means “safer than the average human driver,” because it is necessarily defined with respect to population-level data. At the level of individual risk assessment, a body of research has shown that most drivers perceive themselves to be safer than the average driver (the better-than-average effect). Using an online sample of U.S. drivers, this study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely before they would: (1) feel reasonably safe riding in a self-driving vehicle; (2) buy a self-driving vehicle, all other things (cost, etc.) being equal; and (3) allow self-driving vehicles on public roads. Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of self-driving cars for most drivers.”

3. 3

   Automated Vehicle Crash Rate Comparison Using Naturalistic Data

   VTechWorks (Virginia Tech) (Blanco et al.)Published Jan 1, 2016Checked Oct 3, 2026

   “Third, SHRP 2 NDS data were again used to describe various scenarios related to crashes with no known police report. This analysis considered whether such factors as driver distraction or impairment were involved, or whether these crashes involved rear-end collisions or road departures. Crashes within the SHRP 2 NDS dataset were ranked according to severity for the referenced event/incident type(s) based on the magnitude of vehicle dynamics (e.g., high Delta-V or acceleration), the presumed amount of property damage (less than or greater than $1,500, airbag deployment), knowledge of human injuries (often unknown in this dataset), and the level of risk posed to the drivers and other road users (Antin, et al., 2015; Table 1). Google Self-Driving Car crashes were also analyzed using the methods developed for the SHRP 2 NDS in order to determine crash severity levels and fault (using these methods, none of the vehicles operating in autonomous mode were deemed at fault in crashes).”

4. 4

   Analysis of safety risks in mixed driving of manual and automatic vehicles: multiple perspectives.

   PloS one (He et al.)Published May 15, 2025Checked Oct 4, 2026

   “To improve traffic safety in mixed traffic involving human-driven and autonomous vehicles, this study explored safety risk factors from multiple perspectives. Based on crash reports involving autonomous vehicles (AVs) in the California, United States, the XGBoost algorithm and Shapley additive explanations (SHAP) analysis were used to investigate the factors affecting accident severity. Association rule mining was employed to analyze the factors contributing to emergency braking events, based on field data from driverless taxi operations in China. Additionally, using data collected from questionnaires, the risk perception factors of different traffic participants were examined using the average degree of aggressiveness method. The results of three aspects analysis revealed that risk factors associated with mixed traffic were concentrated in areas such as weekdays, road sections, multiple lanes, roads with central medians, lack of control, and adverse environments. Finally, some safety improvement suggestions are recommended.”

5. 5

   Self-driving car (Wikipedia)

   WikipediaPublished Oct 1, 2026Checked Oct 4, 2026

   “A self-driving car, also known as an autonomous car, driverless car, robotic car, or robo-car, is a car that is capable of operating with reduced or no human input. They are sometimes called robotaxis, though this term refers specifically to self-driving cars operated for a ridesharing company. As of 2026, the term "self-driving" lacks an agreed standard definition and is also subject to commercial advertising and branding considerations. In 2020, Waymo was the first to offer rides in driverless taxis in the operational design domain (ODD) of limited geographic areas, but as of late 2025, no system has achieved full autonomy in all domains - sometimes referred to as "Level 5" on a scale of 0 to 5 levels of automation defined by the global standards organization SAE International, or simply "no driver" as given by the classification system proposed by Mobileye in the US. Following a history of experimentation and development of advanced driver assistance systems (ADAS) after World War II, two main technologies are now primarily used: LiDAR (Light Detection and Ranging), and visual sensors (cameras) which capture images and video like human eyes.”

6. 6

   Characteristics of media-reported road traffic crashes involving low-level automated vehicles in China, 2015-2025.

   Journal of global health (Zhao et al.)Published Aug 28, 2026Checked Oct 3, 2026

   “BackgroundLow-level automated vehicles (LAVs) have emerged as a new public health challenge, but the epidemiological characteristics of LAV-related crashes remain unknown.MethodsBased on media reports collected by the Automated Road Traffic Crash Data Platform (ARTCDP), we analysed the characteristics of LAV-related crashes in China between 1 January 2015 and 31 August 2025.ResultsThe ARTCDP captured 4,669 crashes involving LAVs and 324,869 involving other motor vehicles. Compared to other motor vehicles, LAVs were more frequently reported to crash during nighttime (65.3% vs. 29.1%; P ConclusionsInternet-based media reports detected distinct characteristics of road traffic crashes involving LAVs, meriting the attention of policymakers and law enforcement.”

7. 7

   Autonomous Vehicles and Public Health

   Annual Review of Public Health (Rueda et al.)Published Jan 31, 2020Checked Oct 3, 2026

   “Autonomous vehicles (AVs) have the potential to shape urban life and significantly modify travel behaviors. "Autonomous technology" means technology that can drive a vehicle without active physical control or monitoring by a human operator. The first AV fleets are already in service in US cities. AVs offer a variety of automation, vehicle ownership, and vehicle use options. AVs could increase some health risks (such as air pollution, noise, and sedentarism); however, if proper regulated, AVs will likely reduce morbidity and mortality from motor vehicle crashes and may help reshape cities to promote healthy urban environments. Healthy models of AV use include fully electric vehicles in a system of ridesharing and ridesplitting. Public health will benefit if proper policies and regulatory frameworks are implemented before the complete introduction of AVs into the market.”