OOF-AII-734635
Tesla Model 3 on Autopilot Crashed into a Ford Explorer Pickup, Killing a Fifteen-Year-Old in California
A Tesla Model 3 on Autopilot mode crashed into a pickup on a California freeway, where data and video from the company showed neither Autopilot nor the driver slowing the vehicle until seconds before the crash.
- Event date
- 2019-08-24
- Country
- United States of America
- Severity
- Low
- Evidence state
- Single-source
Assessment status
Generated from available public incident evidence using OOF® governance methodology. It is not an official investigation, legal determination, regulatory finding, certification, or OOF® Approved Assessment.
View source record from AI Incident Database (Responsible AI Collaborative)
Assessment confidence dimensions
- Source corroboration
- Single-source
- Extraction completeness
- High
- Methodological assignment confidence
- High
Measures source-group corroboration only; it does not establish factual truth.
Measures populated source fields after deterministic normalization; it does not measure correctness.
Measures automated assignment support; substantive correctness requires authorized human review.
Known facts from the incident record
- The incident record identifies the event date as 2019-08-24.
- The incident record identifies the location as United States of America.
Reported claims
- A Tesla Model 3 on Autopilot mode crashed into a pickup on a California freeway, where data and video from the company showed neither Autopilot nor the driver slowing the vehicle until seconds before the crash.
Material unknowns
- Independent corroboration is not established in the available incident record.
Primary governance problem
The incident appears to expose a governance problem involving autonomous, execution, classification. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.
Architecture intelligence
ASGA™ — Autonomous Systems Governance Architecture
Primary architecture
The reported autonomous, execution, classification concern falls within the governed questions represented by ASGA™ — Autonomous Systems Governance Architecture.
Governance focus: How should autonomous, execution, classification be governed in the conditions described by the available incident evidence?
Canonical methodology (ParentStandard):
PGA™ — Prediction Governance Architecture
Contributing architecture
The reported into, object concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.
Governance focus: How are methodology, Prediction Basis, assumptions, and applicable conditions transformed into a specific governed prediction?
Canonical methodology (ParentStandard):
CLIA® — Cognitive Governance Intelligence Architecture
Contributing architecture
The reported autonomous concern falls within the governed questions represented by CLIA® — Cognitive Governance Intelligence Architecture.
Governance focus: How should autonomous be governed in the conditions described by the available incident evidence?
Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.
Preliminary governance questions
- How should autonomous, execution, classification be governed in the conditions described by the available incident evidence?
- How are methodology, Prediction Basis, assumptions, and applicable conditions transformed into a specific governed prediction?
- How should autonomous be governed in the conditions described by the available incident evidence?
- What operational authority and controls applied to Tesla at the relevant time?
- What evidence would establish how yes produced or enabled the reported outcome?
Evidence gaps and limitations
- Only one independent source group is represented; corroboration is not established.
- AI technology is not specified in the source record.