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> Doty said the car nearly hit a moving train in November after it approached some tracks after a sharp turn. > He said that the Tesla did not slow down but th

> Doty said the car nearly hit a moving train in November after it approached some tracks after a sharp turn. > He said that the Tesla did not slow down but that he was able to stop, still hitting the crossbar and damaging his windshield. So this guy has been in a similar position before and still let the car barrel towards the train hoping it would stop this time? I don’t buy his story. The fog is an interesting situation too, as non-vision technologies would struggle here too.

actionfromafar 2y ago
Radar struggle in fog?
- IshKebab 2y ago
  I think he meant lidar.
- masklinn 2y ago
  They do not. Lidars do though.
  - pixelesque 2y ago
    Well, depending on the wavelength used and the MFP of the scattering elements (rain, fog droplets in this case) they do degrade a bit, but it'd be very unlikely to have a huge effect under 300 m.
- modeless 2y ago
  Automotive radars struggle to detect objects which are not moving relative to the ground in the range direction. Such detections must be filtered aggressively to avoid false positives from stationary objects such as metal signs near or above the road. At the point where it crossed the road the train was moving sideways relative to the car. In the range direction it was close to stationary relative to the ground (and the crossing arm was entirely stationary, of course). So I think radar would be unlikely to save this case. If the radar was looking to the side, so it could see the train cars approaching at high speed and predict that they would enter the road, then maybe. But this seems like it would also potentially have a lot of false positives e.g. at overpasses. Perhaps for that reason, I believe automotive radars like the ones Teslas used to have generally only point toward the front. And if the train had been stopped in the crossing, as they sometimes are, the radar would be unhelpful no matter where it was pointed.
rob74 2y ago
Overlooking the train is one thing, but overlooking the red flashing lights at the railway crossing that were clearly visible in the video is another thing entirely...
PinguTS 2y ago
But isn't Elon telling the story that it improves overtime from month to month. So you may would have expected that it have learned since November, which is more than 3 month in the past.

actionfromafar 2y ago

Radar struggle in fog?

IshKebab 2y ago
I think he meant lidar.
masklinn 2y ago
They do not. Lidars do though.
- pixelesque 2y ago
  Well, depending on the wavelength used and the MFP of the scattering elements (rain, fog droplets in this case) they do degrade a bit, but it'd be very unlikely to have a huge effect under 300 m.
modeless 2y ago
Automotive radars struggle to detect objects which are not moving relative to the ground in the range direction. Such detections must be filtered aggressively to avoid false positives from stationary objects such as metal signs near or above the road. At the point where it crossed the road the train was moving sideways relative to the car. In the range direction it was close to stationary relative to the ground (and the crossing arm was entirely stationary, of course). So I think radar would be unlikely to save this case. If the radar was looking to the side, so it could see the train cars approaching at high speed and predict that they would enter the road, then maybe. But this seems like it would also potentially have a lot of false positives e.g. at overpasses. Perhaps for that reason, I believe automotive radars like the ones Teslas used to have generally only point toward the front. And if the train had been stopped in the crossing, as they sometimes are, the radar would be unhelpful no matter where it was pointed.

rob74 2y ago

Overlooking the train is one thing, but overlooking the red flashing lights at the railway crossing that were clearly visible in the video is another thing entirely...

PinguTS 2y ago

But isn't Elon telling the story that it improves overtime from month to month. So you may would have expected that it have learned since November, which is more than 3 month in the past.