Pith. sign in

REVIEW 5 cited by

Waymo Public Road Safety Performance Data

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2011.00038 v1 pith:FDUIOGOY submitted 2020-10-30 cs.RO

classification cs.RO
keywords waymoeventsautomatedcollisioncontactdriverhumanroad
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Assessing a Safety Case: Bottom-up Guidance for Claims and Evidence Evaluation

    cs.SE 2025-06 accept novelty 6.0 of 10

    The paper defines scoring rubrics for claim support (procedural and implementation) and evidence status, forming a bottom-up method for ADS safety case credibility assessment.

  2. Fractional Collisions: A Framework for Risk Estimation of Counterfactual Conflicts using Autonomous Driving Behavior Simulations

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A fractional collision metric converts probabilistic human reactions in simulated conflicts into expected injury and damage counts, demonstrated on naturalistic crash reconstructions and 250k miles of ADS data.

  3. The DevSafeOps Dilemma: A Systematic Literature Review on Rapidity in Safe Autonomous Driving Development and Operation

    cs.SE 2025-06 conditional novelty 5.0 of 10

    A systematic review maps 11 clusters of challenges and their proposed solutions for applying DevOps to safe autonomous driving development.

  4. Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

    cs.LG 2025-06 reject novelty 5.0 of 10

    A latent-space extension of MBPO for measuring and mitigating the sim-to-real gap performs inconsistently across MuJoCo perturbations, and its gap metric is non-monotonic.

  5. Trajectory Prediction in Dynamic Object Tracking: A Critical Study

    cs.CV 2025-06 conditional novelty 1.0 of 10

    A survey of dynamic object tracking and trajectory prediction that identifies gaps and proposes a conceptual feedback-loop integration, but presents no formal model or experimental validation.

Pith tools