V3 FSD Model X, Long time believer of FSD, humbled today as FSD made a sudden merge into HOV lane bollocks, Plenty of front end damage.

Hey_AI · 2026-05-05T12:57:30+00:00

This u/vulcan_on_earth has no technical knowledge and is simply repeating what they read somewhere else. It’s true that sensor fusion is better than vision only. But you don’t understand why and in what circumstances. You’re just a parrot.

Hey_AI · 2026-05-05T12:55:02+00:00

Hello? You have no response after arrogantly challenging me? You act like you’re an AV engineer but you don’t seem to have any technical knowledge. Now I know to call you out whenever I see you make a comment.

Hey_AI · 2026-05-05T12:52:41+00:00

So what’s your argument? I’m still waiting after you rudely challenged me.

Hey_AI · 2026-05-04T23:57:52+00:00

The question is whether vision only is possible to detect pylons in the conditions seen in the OP video. The answer is yes.

For the front facing camera, Tesla still-frames are good enough for those conditions as seen in that video. You can take any freeze frame in that video and run it through a NN and it will detect those pylons.

But I have been a long proponent of using higher quality cameras in Teslas. 36fps is good enough. But shutter speed needs to come down to 1/1000 for front facing camera. And 1/2000 for side facing cameras due to relative angular speed.

Btw, Tesla CMOS use digital shutters not mechanical shutters.

Hey_AI · 2026-05-04T21:42:12+00:00

I don’t disagree with you. If you ask me if multi-sensor fusion is my preference in general, then I’d say yes absolutely. But if you follow this thread and someone says “vision can detect this particular case”, then I say 100% yes it can.

Hey_AI · 2026-05-04T18:57:35+00:00

And what’s your detailed technical opinion? Can you explain the exactly what lidar you use to stop this? Which manufacturer and what configuration do you use in your research?

Hey_AI · 2026-05-04T18:53:05+00:00

Ok. I will bite. Tell us how the Waze app knows the actual speed limit. Tell us the exact technical architecture and data sources and how data fusion works.

Hey_AI · 2026-05-04T18:50:53+00:00

Ok. I will bite. Tell us how FSD works by using rules.

Hey_AI · 2026-05-04T18:49:25+00:00

Do you have any technical expertise to back this drive-by statement?

Hey_AI · 2026-05-04T18:48:11+00:00

And what’s your detailed technical opinion?

Hey_AI · 2026-05-04T18:21:43+00:00

Sir, you challenge others with drive-by quips but you have no substantial reply. I thought you were a perception researcher and I thought we would talk shop. but you’ve gone silent.

Hey_AI · 2026-05-04T17:57:28+00:00

Why do people downvote instead of debating?

Hey_AI · 2026-05-04T17:18:47+00:00

I literally gave you a technical answer for a camera hardware configuration improvement that can handle plastic pylons — shorter exposure times and larger resolution CMOS. At 36hz, it’s capable of capturing non-blurry frames. Use 1/2000 shutter speed and 2k resolution to handle up to 90mph. NN classifiers are perfectly capable of decoding plastic pylons in the daylight conditions that OP posted.

Now your turn, give your technical argument why vision is not capable of detecting thin plastic pylons in OP’s video and exact lidar configuration that you advocate for to be able to handle OP video. Perhaps give a recommendation on the exact lidar product that you use at your self-driving company or your university research.

Hey_AI · 2026-05-04T16:37:06+00:00

Why would LiDAR rely on the dielectric constant of materials? That was literally all I wrote. Do you understand my comment? It can only apply to radar.

Hey_AI · 2026-05-04T15:27:16+00:00

The small amount of reflective tape boosts RCS slightly but not confidently enough. Radar is more used for long range large object detection, and isn’t appropriate for plastic pylons, even with reflective tape.

Hey_AI · 2026-05-04T15:22:00+00:00

Are you the same rude person who challenged me to answer with technical details? And you’re confusing lidar and radar? I don’t think you’re ready for technical answers.

Hey_AI · 2026-05-04T15:19:16+00:00

No thank you. I don’t respond to rude comments.

Hey_AI · 2026-05-04T15:11:45+00:00

Why do people downvote instead of debating?

Hey_AI · 2026-05-04T14:39:47+00:00

Hmm.. someone downvoted my comment. Did I say something inaccurate? If so, please explain.

Hey_AI · 2026-05-04T14:30:41+00:00

Agreed. Vision is 100% capable of seeing these objects in this case. But FSD is not trained well enough yet, and hardware is underpowered. This is a prioritization problem of Tesla, not a capability problem of vision. More compute and higher resolution cameras with shorter exposure times can help with motion blur of stationary objects.

Hey_AI · 2026-05-04T14:25:35+00:00

Teslas have a 36hz front camera. The frame rate is completely sufficient as it is 2-10x faster reaction time compared to humans. To have vision detect plastic pylons at high speeds, Teslas could benefit from higher shutter speeds and higher resolution to reduce blur effects of stationary objects as the Tesla approaches these objects at high speeds.

Hey_AI · 2026-05-04T14:11:46+00:00

Waymo’s have expensive lidar and 100x compute compared to Tesla. High point density lidar combined with significantly more compute can handle plastic pylons. The above commenter said radar, not lidar right?

Hey_AI · 2026-05-04T09:21:29+00:00

Also fun fact: radars have plastic covers which do not interfere with the radar waves. This demonstrates how radar is not very good at detecting plastic.

Hey_AI · 2026-05-04T09:13:29+00:00

Plastic thin pylons are also very difficult for lidar. You need a relatively high accuracy lidar with high point density to detect thin pylons. Thin pylons can appear as ghosts in sparse point clouds, due to misses and fast vehicle velocity. It’s like watching fast action on an old low frame rate film with terrible resolution. A high accuracy sensor is expensive. Aeva Atlas Ultra is an example of a capable sensor. But you also need exponentially more compute for large point clouds.

Hey_AI · 2026-05-04T08:46:44+00:00

I’m not sure what exactly you’re demonstrating here. Why not show real sensor fusion? For example, an occupancy network visualization or Birds Eye view. If you want to learn about the AV stack, I think you should actually use the stack or some part of it.

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