On the surface, the root cause of the incident was not a hardware or software malfunction within the vehicles themselves, but rather the disorder that emerged in the city’s traffic system after electricity was lost. Traffic lights at several major intersections went dark, pushing pedestrians and vehicles into a highly unstructured and chaotic flow. Footage circulating on social media showed Waymo vehicles stationary with hazard lights activated, clearly adopting a conservative strategy in the face of uncertainty. This reaction underscores a key limitation of current autonomous driving systems: when they rely heavily on traffic signals and predefined rules, sudden and non-standard scenarios can still create decision-making bottlenecks.
The experiences of actual passengers further revealed the constraints of the system’s logic. Michele Riva, a passenger who works as an artificial intelligence engineer, noted that the vehicle was able to continue driving normally in areas with relatively simple traffic conditions, even when pedestrians were crossing the street. However, as the car approached a high-density intersection where traffic signals had completely failed, the system chose to stop outright, without providing passengers with immediate explanations or expectations for how the situation would be handled. This indicates that Waymo’s decision-making model, in terms of risk assessment, continues to adopt an extremely conservative—almost immobilizing—approach when multiple sources of uncertainty arise simultaneously.
From an operational perspective, the incident also exposed the pressure placed on customer support and fleet dispatch systems during large-scale abnormal events. Riva attempted to contact customer support but abandoned the effort due to excessive wait times, ultimately choosing to exit the vehicle and walk home. This reflects the fact that when a large number of vehicles simultaneously encounter abnormal conditions, backend support systems may still lack sufficient real-time response capacity. Waymo’s subsequent decision to suspend services across at least seven Bay Area cities on Sunday further highlighted its choice to prioritize safety by broadly scaling back operations to prevent additional incidents.
Notably, Waymo emphasized that it was working closely with city officials and hoped to restore services as soon as possible. This underscores how autonomous driving is no longer merely a technical challenge for automakers or technology companies, but a systems-level undertaking deeply embedded in urban governance and public infrastructure. When any single link—whether electricity, traffic signals, or communications—fails, the stability of autonomous driving services is directly affected. This raises the bar for system resilience as cities look to introduce autonomous vehicles at scale in the future.
At the level of industry competition, Tesla CEO Elon Musk quickly stated on social media that his company’s robotaxi services were unaffected by the power outage. While no technical details were provided, the remark inevitably shifted attention toward differences in how various autonomous driving approaches depend on external infrastructure. Waymo is known for its high level of sensor fusion and stringent safety logic, which may also make it more prone to halting operations in extremely chaotic environments. Whether other players can truly continue operating under the same conditions remains a question that requires real-world evidence and regulatory scrutiny.
Overall, this incident was not merely an operational disruption, but a live lesson in the “safety boundaries” of autonomous driving and the gap between technological capability and societal expectations. As Michele Riva observed, many passengers can understand the safety rationale behind a system that prefers to stop rather than take risks, yet they also hope future systems will offer clearer communication and more flexible responses in similar situations. For the automotive industry as a whole, this represents an unavoidable and critical issue that must be addressed as autonomous driving moves from technical validation toward large-scale commercial deployment.