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During a major power outage in San Francisco on December 20, 2025, Waymo's autonomous vehicle fleet caused significant traffic congestion. The vehicles' safety protocols, which triggered remote confirmation checks when encountering dark traffic signals, were overwhelmed by the scale of the outage. This resulted in vehicles stalling at intersections, prompting city officials to order the fleet off the road to clear access for emergency responders.
Waymo's autonomous vehicle fleet reportedly contributed to traffic congestion in San Francisco after a PG&E substation fire cut power to nearly one-third of the city on December 20, 2025. As traffic signals went dark, Waymo vehicles requested remote confirmation checks at scale, creating delays that added to gridlock and prompted city officials to order the fleet off the road. Waymo later paused service and announced software and emergency-response updates.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
The incident represents a lack of robustness under extreme, large-scale infrastructure failure conditions, where the AI system's safety protocols became overwhelmed and caused the vehicles to stall.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The traffic congestion was caused by the Waymo Driver AI system's safety protocols triggering excessive remote confirmation requests during a post-deployment power outage, which was an unintentional outcome of its design.
During a San Francisco power outage, Waymo's autonomous vehicle fleet suffered a systemic safety-protocol failure, stalling at dark intersections and causing gridlock that impeded emergency responders. While local in scope, the incident highlights minor but notable vulnerabilities in critical transportation infrastructure when integrating autonomous systems that rely on remote human oversight.
The report describes Waymo's self-driving cars operating independently in real-time, responding to environmental inputs (dark traffic signals) using learned patterns and safety protocols without explicit human planning, making it an Autonomous system.
High Risk: The AI system is used in critical infrastructure (specifically road traffic management and transport), which has significant implications for public safety and emergency responder access during a crisis.
The Waymo Driver is designed to operate fully autonomous vehicles to transport passengers safely through city streets without a human driver.
The reports from multiple reputable news sources and Waymo's official blog provide consistent, detailed accounts of the technical mechanism behind the failure (the remote confirmation check backlog) and the operational impact (traffic congestion and service suspension). There is high agreement on the facts of the incident.
One could argue the primary cause was the utility power outage rather than the AI, but the reports clearly focus on how the AI's specific safety protocols uniquely contributed to the traffic gridlock.
Technology
Remote Confirmation Backlog
High volume of requests overwhelmed remote operators, delaying vehicle response.
Lack of Local Outage Context
Vehicles lacked regional power outage context to navigate autonomously.
Conservative Fallback Design
Vehicles default to stopping and requesting checks at dark signals.
Data Inputs
Missing Real-Time Outage Data
Vehicles had no direct data feed about regional grid power status.
Dark Traffic Signal Detection
Encountering dark signals triggered safety checks instead of decisive driving.
Human Factors
Operator Overload
Remote assistance staff could not handle the sudden spike in requests.
Manual Traffic Control Conflicts
Vehicles struggled to coordinate with manual police traffic management.
Process and Methods
Inadequate Scaling Protocols
Confirmation checks worked for small outages but failed at city-wide scale.
Delayed Service Suspension
Service was suspended only after gridlock and mayoral intervention occurred.
Management
Insufficient Risk Assessment
Management failed to anticipate the impact of a large-scale power outage.
Underprepared Emergency Protocols
Emergency plans did not account for coordinated fleet-wide pull-overs.
Regulatory Environment
Lack of Emergency AV Guidelines
No established regulatory standards for AV behavior during power outages.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
