Tesla on Autopilot Collided with Parked Fire Truck on California Freeway

Laws on record for this risk area

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 instruments on record cover this risk area.

• India DPDP Act India · Partially applicable · binding
• Law on Artificial Intelligence (2025) Kazakhstan · Adopted · binding
• Law No. 132/2025 on artificial intelligence Italy · In force · binding
• EU AI Act European Union · Partially applicable · binding
• Texas Responsible AI Governance Act (TRAIGA) Texas (United States) · In force · binding

Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (4)

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

1. 23 Jan 2018
   Tesla Police Blotter News - Tesla Driver Hits Parked Firetruck - Blames Autopilot

   torquenews.com · John Goreham
2. 3 Sep 2019
   Tesla car was on Autopilot when it hit a Culver City firetruck, NTSB finds

   latimes.com · Associated Press
3. 4 Sep 2019
   Tesla Driver Was on Autopilot Eating a Bagel and Hit a Fire Truck

   caranddriver.com · Clifford Atiyeh
4. 12 Sep 2019
   NTSB Issues Report on Tesla Crash with Culver City Fire Engine

   culvercityobserver.com · Culver City Observer

Who was involved

Alleged deployer

Tesla

Alleged developer

Tesla

Alleged harmed party

Tesla Drivers, Culver City Fire Department

Classification (MIT AI Risk Repository taxonomy)

Risk domain

AI system safety, failures, and limitations

Risk subdomain

7.3 Lack of capability or robustness

Causal entity

AI

Intent

Unintentional

Timing

Post-deployment

Harm level

—

Sectors

—

Countries

—

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)

• 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 )

• 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 )

• 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

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• 31 Mar 2026Baidu Apollo Go Robotaxis Stopped in Traffic During Reported System Failure in Wuhan, Stranding Some Passengers
• 28 Feb 2026Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School
• 26 Feb 2026Claude Code Agent Reportedly Deleted DataTalks.Club Production Infrastructure, Database, and Snapshots via Terraform
• 17 Feb 2026Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention

Other incidents involving Tesla

• 17 Feb 2025Tesla Cybertruck Operating in Full Self-Driving Mode Reportedly Crashes into Pole in Nevada After Failing to Merge
• 7 Jan 2025Tesla's 'Actually Smart Summon' Feature Reportedly Linked to Multiple Parking Lot Collisions
• 23 Jun 2023Tesla on Autopilot Struck Parked Work Truck on Highway in Pennsylvania
• 15 May 2023Tesla Failed to Yield to Detected Pedestrian on Crosswalk, Reportedly Violated Traffic Law
• 17 Mar 2023Tesla Allegedly on Autopilot Struck High School Student Exiting School Bus
• 28 Dec 2022Tesla Driver Put Car on Autopilot Before Falling Asleep in Germany