Waymo pauses Atlanta service as its robotaxis keep driving into floods
Waymo has suspended its robotaxi service in Atlanta after several vehicles drove into flooded streets and, in one case, became stuck, renewing scrutiny of autonomous cars’ ability to handle bad weather and rare edge cases. Commenters debate whether self‑driving systems should operate at all in conditions like flash floods, how they compare to human drivers in real‑world safety, and whether it’s acceptable for such services to pause during extreme events. The thread also branches into questions about sensor choices (lidar vs cameras), long‑term viability and cost of robotaxis, and whether investment would be better directed toward traditional public transit.
Flooding behavior and sensing limits
- Core issue: Waymo cars are repeatedly entering flooded streets, sometimes stalling or being swept, leading to service pauses in Atlanta (and earlier San Antonio, others).
- Posters debate why cars can’t distinguish puddles vs dangerous floods, something many humans intuitively avoid.
- Proposed technical approaches:
- Use existing lidar maps plus elevation data to infer water depth.
- Detect “holes” or surface irregularities in point clouds.
- Compare road vs water surface roughness, or add IR / radar (seen as expensive).
- Others argue the only robust solution is extreme caution: stop when water covers markings or exceeds some size, reroute, or suspend service during flood risk—yet flash floods can precede official warnings.
How AVs compare to humans
- Many note humans also misjudge water depth, regularly flooding engines or drowning; flooded-road deaths are common in some regions.
- Counterpoint: taxi drivers with passengers rarely take those risks; passengers expect professional-level caution.
- Some say if Waymo drives into floods at all, it fails the “better than humans” bar; others respond that one or two such incidents among millions of miles is still a net safety gain.
Traffic, evacuation, and coordination
- Discussion on whether universal self-driving could cut gridlock, especially in hurricane evacuations: smoother flow, synchronized starts at lights, tighter following distances, reversible lanes.
- Skeptics note finite road capacity, weather-reduced capacity, and mixed traffic with humans; full coordination across vendors and no network dependence in disasters is seen as a hard unsolved problem.
Sensors, ML, and system design
- Debate over lidar+radar vs vision-only:
- Pro-lidar: crucial redundancy in bad weather and to avoid camera failures (glare, strobes, shadows).
- Pro-vision: simpler training (closer to human inputs), fewer fusion failure modes, lower cost.
- Some suggest integrating LLM-style “reasoning” for long-horizon decisions (e.g., don’t drive into obviously abnormal situations); others dismiss this as hand-wavy.
Safety expectations and public perception
- Strong view that AVs must be substantially safer than humans because rare AV failures are highly salient, unlike millions of human accidents.
- Others stress regulators/insurers will look at aggregate statistics, not anecdotes, and that AVs don’t drink, text, or fatigue—huge advantages even if they fail in exotic edge cases.
Economics, rollout strategy, and alternatives
- Split between optimism (“early-stage slog; edge cases will be trained away; rollout city-by-city is exactly how you learn”) and pessimism (“20 years in, still failing common weather; not obviously a viable business”).
- Some think Waymo-like services will be niche in dense, affluent markets; others expect eventual ubiquity and hardware cost drops.
- Strong contingent argues money would be better spent on public transit (trains, buses, BRT), with AVs at best a complement for last-mile rather than a replacement.