On June 11, 2018, a Cruise autonomous vehicle was rear-ended by another Cruise vehicle being operated in conventional mode by a human employee in San Francisco. The incident resulted in minor property damage to both vehicles, with no injuries reported. The event was documented in a collision report submitted to the California Department of Motor Vehicles.
In San Francisco, an autonomous Cruise Chevrolet Bolt collided with another Cruise vehicle driven by a Cruise human employee, causing minor scuffs to the cars but no human injuries.
Risk classification
- Primary risk domain: 7 AI system safety, failures, & limitations
- Primary risk subdomain: 7.3 Lack of capability or robustness
The incident highlights the challenges of autonomous vehicles operating in complex human environments, where the AI's overly cautious programming can lead to unexpected interactions and collisions with human drivers.
Causal factors
- Entity: Human
- Intent: Unintentional
- Timing: Post-deployment
The collision was caused by a human employee driving a Cruise vehicle in conventional mode who rear-ended an autonomous Cruise vehicle. The human driver did not intend to cause the accident, which occurred during post-deployment testing on public streets.
EU AI Act risk tier
High Risk: The system is an autonomous vehicle operating in public traffic, which falls under critical infrastructure and safety-critical transport systems. The report describes it operating on public streets where failures can have significant implications for physical safety.
AI system and alleged parties
- AI system: Cruise Chevrolet Bolt (General Motors)
- AI purpose: Autonomous Driving; Navigation Assistant
- Behaviour type: Autonomous
- Alleged developer: Cruise
- Alleged deployer: Cruise
- Alleged harmed parties: Cruise vehicles, Cruise driver employee
Harm severity
Severity ratings are based on this scale.
Highest direct severity in any category: Negligible. Severity is scored from Negligible to Catastrophic in each harm category, for harm the reports describe as caused directly or indirectly by the AI system.
- Physical: direct Negligible, indirect Negligible
- Infrastructure: direct Negligible, indirect Negligible
- Property: direct Negligible, indirect Negligible
- Financial: direct Negligible, indirect Negligible
- Environmental: direct Negligible, indirect Negligible
- Malicious content: direct Negligible, indirect Negligible
- Differential treatment: direct Negligible, indirect Negligible
- Civil rights: direct Negligible, indirect Negligible
- Democracy: direct Negligible, indirect Negligible
- Privacy: direct Negligible, indirect Negligible
- Psychological: direct Negligible, indirect Negligible
- Epistemic: direct Negligible, indirect Negligible
- Child sexual exploitation and abuse: direct Negligible, indirect Negligible
People affected
- Occurrences reported: 1
- People reportedly exposed: 2
Potential causes
Management
- Safety Over Smoothness Policy: Management prioritized conservative safety behaviors over natural driving flow.
Technology
- Overly Cautious Programming: AV programmed to be hyper-cautious, causing unexpected braking for humans.
- Strict Traffic Law Adherence: AV stops fully and abruptly, mismatching typical human driving expectations.
Data Inputs
- Complex Urban Edge Cases: San Francisco streets present dense, unpredictable data inputs for the AV.
Human Factors
- Human Driver Error: The trailing vehicle's human driver failed to react to the AV's stop.
- Human-AV Interaction Gap: Humans do not expect vehicles to follow traffic laws to a strict T.
Process and Methods
- Mixed-Mode Testing: Testing autonomous and human-driven vehicles in close proximity.
Regulatory Environment
- Self-Reporting Regulations: Allows companies to self-report minor incidents without active oversight.
Information quality
- Classification confidence: High
- Reason for confidence: The reports provide clear, consistent details about the specific collision on June 11, 2018, including the modes of operation of both vehicles, the lack of injuries, and the minor nature of the property damage. The facts are well-documented via official DMV reports.
A minor, non-injury traffic collision involving two GM Cruise test vehicles in San Francisco. The incident represents a routine testing mishap with negligible national security implications.
- Overall national security impact: Negligible
- Scope: Single nation
- Primary target: No clear primary
- Alleged perpetrator: Unknown
Threat characteristics
- Imminence: Long-term. The incident was a minor localized event in 2018 with no ongoing threat or escalation potential.
- Autonomy: Human-supervised. The autonomous vehicle operated under testing conditions which typically involve safety drivers and human oversight.
- Novelty: Established threat. Minor collisions involving autonomous test vehicles on public roads are well-documented and established events.
Impact by dimension
- Physical security: Negligible. Minor fender bender between two test vehicles with no injuries, physical threats, or critical infrastructure impact.
- Information security: Negligible. No information warfare, intelligence compromise, or data theft occurred in this incident.
- Sovereignty: Negligible. No impact on sovereign governance, elections, or government functions.
- Economic security: Negligible. Minimal economic impact, limited to minor repair costs for two company-owned test vehicles.
- Societal stability: Negligible. No implications for societal stability, human rights, or civil liberties.
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