Waymo updates 3,800 robotaxis after they 'drive into standing water'

Standing Water as a Hard Perception Problem

• Many note that distinguishing wet pavement, shallow puddles, and deep or moving water is genuinely hard, even for humans.
• Others dispute that humans “frequently” drive into floodwaters, saying it’s rare relative to total miles driven and more tied to specific regions and conditions.
• Examples from Texas, England, rural US, and fords/low-water crossings underline how common dangerous water situations can be in some areas.

Mapping, HD Maps, and a Dynamic World

• Waymo is described as heavily reliant on HD lidar-based maps of service areas.
• Some argue this mapping could help infer water depth by comparing current readings to stored road geometry.
• Others stress maps go stale quickly due to construction, lane shifts, floods, sinkholes, earthquakes, etc., and real-time aggregation and distribution of changes is unsolved at scale.

Sensors and Technical Approaches

• Suggestions: dedicated water / wading sensors, ultrasound, float switches, moisture sensors, radar, world-model-based inference, or inferring from vehicle deceleration and crowdsourced phone sensor data.
• Concerns: many sensors only detect depth after entering water; condensation, salt, bumps, and interference complicate designs.
• Lidar often treats standing water like a mirror; multi-return lidar may sometimes see both surface and road.
• Debate over lidar+camera vs camera-only: more sensors can help but also dilute engineering focus; fusion and sensitivity/specificity trade-offs are nontrivial.

Safety, Edge Cases, and AV vs Human Drivers

• Some see this as an expected edge case; software can be patched fleetwide, leading to long-term safety improvements humans don’t get.
• Others highlight regressions and novel edge cases will always exist, and “stopped car” failure modes can still be dangerous (e.g., in floods, on highways, on tracks).
• Comparisons to Tesla: anecdotes show FSD sometimes avoids water and sometimes aims for it, implying remaining model/data limitations.

“Recall” Terminology and Regulation

• Multiple comments note that “recall” here means a safety defect plus a fix, often just an over-the-air update.
• Several argue the term misleads the public, conflating catastrophic hardware defects with software patches, but others emphasize it is a regulated, legally defined term that signals a safety issue regardless of fix method.