Tesla reports another Robotaxi crash

Accident Rates and Statistical Debates

• Thread centers on claims that Tesla’s Robotaxis in Austin crash about once every 40k miles vs ~500k miles per reported human crash.
• Several commenters question the 500k figure, noting it likely only covers police‑reported crashes and excludes minor “hit a pole / garbage can” incidents, making the comparison imperfect.
• Others stress that, even with caveats, 7–8 crashes across ~20 robotaxis in a short period is alarmingly high, especially with professional supervisors.
• Some argue the sample is too small for strong conclusions and that confidence intervals and better stratification (e.g., urban vs highway) are missing.

Sensors: Cameras vs Lidar/Radar

• Major debate over Tesla’s camera‑only approach vs multi‑sensor (lidar/radar) stacks.
• Critics say starting with fewer sensors is backwards: use everything, make it work, then simplify. They highlight cameras’ weaknesses in fog, rain, glare, and low light.
• Defenders argue simplifying the sensor stack reduces engineering complexity, avoids ambiguous multi‑sensor conflicts, and can speed development; they claim cameras are the most versatile sensor.
• Counterpoint: multi‑sensor fusion is a mature field and can yield strictly better perception despite added latency and complexity.

Tesla vs Waymo Approaches and Performance

• Waymo is cited as having >100M driverless miles and large reductions in crash and injury rates vs humans; some users report frequent reliable use.
• Others note Waymo still has issues (school bus recall, parade and crime‑scene blockages) and operates only in limited geofenced areas and benign climates, so “problem solved” is disputed.
• Disagreement over whether Waymo counts as “completely self‑driving” given its remote “fleet response” system that can propose paths or interventions.
• Comparisons in Austin suggest Tesla’s incident rate is roughly 2x Waymo’s per mile; one commenter claims ~20x if only Robotaxis are counted, but this is not firmly resolved.

Data Transparency and Trust

• Tesla’s heavy redaction of NHTSA incident reports is widely criticized; it prevents detailed severity analysis and fuels suspicion they’re hiding worse outcomes.
• Waymo’s more granular public data enables peer‑reviewed safety studies; similar analysis is impossible for Tesla, eroding trust in its safety claims.

Ethics, Regulation, and Public Risk

• Some see deploying high‑crash‑rate systems on public roads as prioritizing corporate interests over public safety, likening it to earlier auto safety scandals.
• Others view a limited, supervised rollout with only one hospitalization so far as a “not terrible” early phase of an iterative technology, but many insist that anything less safe than a human driver violates the core social contract for self‑driving cars.

Media Bias and Source Skepticism

• A subset of commenters argue the article outlet is hostile to Tesla, cherry‑picks Tesla incidents, and uses misleading language (e.g., calling every contact a “crash”).
• Opponents reply that Tesla could easily dispel doubts by releasing unredacted data and that the bigger unsubstantiated claims are Tesla’s own FSD safety numbers, which lack independent audit.