AIPolicyTracker

AI incident ·

Google Waymo Vehicle and Bus Allegedly Collide in California

27 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Google allegedly harmed Mountain View Municipal Bus Passengers and Mountain View Municipal Bus.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
27 reportsAug 2008 - May 2018

What happened

On February 14, 2016, a Google autonomous test vehicle partially responsible for a low-speed collision with a bus on El Camino Real in Google’s hometown of Mountain View, CA.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case in the United States.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (27)

Titles link to the original publisher; report text is not reproduced here.

  1. Google's self-driving car caused an accident, so what now?
    cnbc.com · Phil Lebeau, Mark Wilson, Joe Belanger
  2. Google car hits bus, first time at fault
    usatoday.com · Marco Della Cava
  3. Google’s Self-Driving Car Probably Caused Its First Accident
    technologyreview.com · Rachel Metz, Will Knight, Douglas Heaven
  4. Google Self-Driving Car Crashes Into Bus
    learningenglish.voanews.com · Mario Ritter
  5. Video shows Google self-driving car hit a bus
    cbsnews.com · Associated Press

Who was involved

Alleged deployer
Google
Alleged developer
Google
Alleged harmed party
Mountain View Municipal Bus Passengers, Mountain View Municipal Bus

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
AI tangible harm event
Sectors
transportation and storage
Countries
US

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Google

Source record: incident #71 on the AI Incident Database · all 27 reports