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Google and Delphi Self-Driving Prototypes Allegedly Involved in Near-Miss on San Antonio Road, Palo Alto

12 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Google and Delphi Technologies allegedly harmed Delphi Technologies and John Absmeier.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
12 reportsMay 2015 - Sep 2017

What happened

A Delphi self-driving Audi Q5 prototype reportedly aborted a lane change after a Google self-driving Lexus RX400h allegedly cut in front of it on San Antonio Road in Palo Alto, California. According to Delphi executive John Absmeier, the Delphi car "took appropriate action" to avoid collision. No crash or injuries occurred.

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 (12)

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

  1. Google, Delphi Self-Driving Cars Getting Dinged in California
    nbcwashington.com · Justin Pritchard
  2. Google's self-driving Lexus cuts off self-driving Audi
    fortune.com · Benjamin Snyder
  3. Two rival self-driving cars have close call in California
    reuters.com · Paul Lienert
  4. Self-driving cars from rivals Google, Delphi in close call
    money.cnn.com · Chris Isidore
  5. Near-miss between self-driving cars shows computers are smarter than us
    dailydot.com · Mike Wehner
  6. Two rival self-driving cars have close call in California
    reuters.com · Paul Lienert
  7. Google and Delphi self-driving cars in near miss
    ibtimes.co.uk · Mary Papenfuss
  8. Robo-car wars: Delphi's near crash, prang, wallop with Google DENIED!
    theregister.co.uk · Kelly Fiveash
  9. Delphi Changes Story, Says Google Self-Driving Car Did Not Nearly Crash Into Audi
    sitepronews.com · Jennifer Cowan
  10. Driverless Car Maker Denies Claim of Near-Miss With Google Car
    time.com · Sarah Begley
  11. Google and Delphi Self Driving Cars Clash In Near Miss Accident
    wiproo.com · Contextual Feed
  12. Google's Driverless Car Almost Hit Delphi's Autonomous Vehicle, Report Says
    dmv.com · Kirsten Rincon

Who was involved

Alleged developer
Google, Delphi Technologies
Alleged harmed party
Delphi Technologies, John Absmeier

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
none
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

Source record: incident #25 on the AI Incident Database · all 12 reports