Tesla Autopilot Allegedly Malfunctioned in a Non-Fatal Collision in Greece

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 Greece.

• 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 domain and country to the instruments recorded here. 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. 25 May 2018
   Tesla Model 3 unofficial road trip ends in crash, driver blames Autopilot

   electrek.co · Fred Lambert
2. 26 May 2018
   Facebook Post: @tsla3

   facebook.com · Tesla Model 3 Road Trip
3. 28 May 2018
   Another Tesla Autopilot Crash Has Wrecked A Model 3 In Greece

   carthrottle.com · Matt Kimberley
4. 29 May 2018
   Greece: Driver claims autopilot malfunction in Tesla crash

   apnews.com · Costas Kantouris

Who was involved

Alleged deployer

Tesla

Alleged developer

Tesla

Alleged harmed party

You You Xue, Tesla Drivers

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

AI tangible harm event

Sectors

transportation and storage

Countries

GR

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

• 15 May 2026Purported AI Name-Reading System Reportedly Skipped and Misannounced Graduates at Arizona's Glendale Community College Commencement
• 24 Apr 2026PocketOS Production Database Was Reportedly Deleted by Cursor AI Agent Running Claude Opus 4.6
• 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