Tesla Reports Two More Fatal Autopilot Crashes to NHTSA

Photo: Tesla

Last year, the National Highway Traffic Safety Administration demanded that automakers turn in their driver-assist crash data for analysis. When the information rolled in, it showed a single automaker as an incredible outlier, with far more crashes than others: Tesla. Recently, the automaker’s high scores haven’t been getting any lower, and it seems that trend continued through last month.

For the month ending October 15 (because government can’t make things easy for us by using actual calendar months) Tesla added two more fatal crashes to its driver-assist roster. Oddly, however, only one crash actually occurred during that period — the other was nearly a full year ago.

Photo: Tesla

The administration doesn’t differentiate between Tesla’s Autopilot and FSD Beta driver-assist products, as both function at SAE Level 2, so it’s unclear which software each car was running at the time of the collision. What is known is that both crashes involved Tesla Model 3s, both occurred in California, and both involved a fatality.

Frustratingly, there’s little information to work from beyond that. Earlier incidents state whether they involved pedestrians or other motorists, but these two seem to be extenuating circumstances. The earlier crash, from December 2021, refers to a “narrative” for details — only for that field to be entirely redacted from NHTSA.

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The other crash, from September of 2022, appears to be too new for much data to exist. Much of the relevant information, from telemetry data to whether the occupants had their seatbelts buckled, is simply left blank in the NHTSA database. Given that it’s only a preliminary report, however, it’s likely more data will roll in with time.

While we may not have every last detail, we do know that this adds at least two more deaths to the Tesla toll. Each one serves as a reminder for drivers, to be highly aware of what their driver-assist systems are doing — and always be ready to intervene if something goes wrong.