# Waymo Public Road Safety Performance Data arXiv: https://arxiv.org/abs/2011.00038 ยท HTML: https://mlsanity.com/paper/2011.00038/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - First posted on arXiv 30 Oct 2020. ## Built on by (most cited first, 24 of 24) - [LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory](https://mlsanity.com/paper/2603.03269/) (2026) - [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025) - [Dynamically Conservative Self-Driving Planner for Long-Tail Cases](https://mlsanity.com/paper/2305.07497/) (2023) - [CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving](https://mlsanity.com/paper/2405.09111/) (2024) - [Reimagining an autonomous vehicle](https://mlsanity.com/paper/2108.05805/) (2021) - [Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2201.06539/) (2022) - [Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection](https://mlsanity.com/paper/2307.01462/) (2023) - [Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing](https://mlsanity.com/paper/2106.00873/) (2021) - [A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness](https://mlsanity.com/paper/2308.01050/) (2023) - [What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence](https://mlsanity.com/paper/2409.05731/) (2024) - [Scene-Aware Explainable Multimodal Trajectory Prediction](https://mlsanity.com/paper/2410.16795/) (2024) - [Lane Change Decision-Making through Deep Reinforcement Learning](https://mlsanity.com/paper/2112.14705/) (2021) - [MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles](https://mlsanity.com/paper/2310.05290/) (2023) - [Flow to Rare Events: An Application of Normalizing Flow in Temporal Importance Sampling for Automated Vehicle Validation](https://mlsanity.com/paper/2407.07320/) (2024) - [Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling](https://mlsanity.com/paper/2506.12735/) (2025) - [DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning](https://mlsanity.com/paper/2606.19656/) (2026) - [A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research](https://mlsanity.com/paper/2407.15868/) (2024) - [Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication](https://mlsanity.com/paper/2501.17329/) (2025) - [3D Gaussian Splatting against Moving Objects for High-Fidelity Street Scene Reconstruction](https://mlsanity.com/paper/2503.12001/) (2025) - [Trajectory Prediction in Dynamic Object Tracking: A Critical Study](https://mlsanity.com/paper/2506.19341/) (2025) - [Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation](https://mlsanity.com/paper/2603.20234/) (2026) - [Agentic Driving Coach: Robustness and Determinism of Agentic AI-Powered Human-in-the-Loop Cyber-Physical Systems](https://mlsanity.com/paper/2604.11705/) (2026) - [Self-Aware Active Learning Enables Continual Improvement in Autonomous Driving](https://mlsanity.com/paper/2608.29772/) (2026) - [When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systems](https://mlsanity.com/paper/2609.13642/) (2026) ## Abstract (arXiv) Waymo's mission to reduce traffic injuries and fatalities and improve mobility for all has led us to expand deployment of automated vehicles on public roads without a human driver behind the wheel. As part of this process, Waymo is committed to providing the public with informative and relevant data regarding the demonstrated safety of Waymo's automated driving system, which we call the Waymo Driver. The data presented in this paper represents more than 6.1 million miles of automated driving in the Phoenix, Arizona metropolitan area, including operations with a trained operator behind the steering wheel from calendar year 2019 and 65,000 miles of driverless operation without a human behind the steering wheel from 2019 and the first nine months of 2020. The paper includes every collision and minor contact experienced during these operations as well as every predicted contact identified using Waymo's counterfactual, what if, simulation of events had the vehicle's trained operator not disengaged automated driving. There were 47 contact events that occurred over this time period, consisting of 18 actual and 29 simulated contact events, none of which would be expected to result in severe or life threatening injuries. This paper presents the collision typology and severity for each actual and simulated event, along with diagrams depicting each of the most significant events. Nearly all the events involved one or more road rule violations or other errors by a human driver or road user, including all eight of the most severe events, which we define as involving actual or expected airbag deployment in any involved vehicle. When compared to national collision statistics, the Waymo Driver completely avoided certain collision modes that human driven vehicles are frequently involved in, including road departure and collisions with fixed objects.