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Comment by g_p | Hacker News Reader

I wonder if Tesla FSD is properly considering a windowed sample of images at a high enough sample rate to see modern high intensity strobe lights used on emergency vehicles.

Clearly the dash cam used is sampling at a high enough rate to see them, but perhaps the Tesla isn't considering the a view of the road ahead across a wide enough period of time to see the hazard lights and understand the vehicle is potentially stationary and alerting drivers about a hazard.

A very naive FSD algorithm working only from the current frame (without looking at past frames) could certainly mess this up, but at what point do we accept this simply isn't fit for purpose - it can't avoid hitting a stationary vehicle, so what hope does it have with slowly moving and other edge cases?

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I'm curious how Tesla FSD works with obstacles that aren't cars or people on roads. Where I drive, I often have to slam on my brakes to avoid hitting deer, turkeys, small rodents, running over squashed skunks, etc. I assume that Tesla FSD would notice some of these obstacles, but it wouldn't be able to notice a deer on the edge of the road and identify it as slowdown-worthy... would it?

Curious if any rural Tesla owners can chime in on this -- it's not exactly a documented feature.

Similarly curious - my understanding is that Tesla w/ FSD effectively drives as a relatively selfish and "reactive" driver, theoretically dealing with anything entering its driving path.

I'd also be interested to know if the Tesla FSD can react to "foreseeable second order" issues that an experienced driver is expected to respond to. For example, a pedestrian crossing (giving priority to pedestrians) where visibility of the waiting pedestrian is obscured by a parked vehicle. A human driver can recognise they lack visibility of the footpath around the crossing (and recognise the crossing by road markings and street furniture), and therefore slow on approach, since there is a risk they may need to stop rapidly.

that principle of "slow on approach, since there is a risk" is something I don't feel like I've seen in any of Tesla's videos.

Even the ones where they are proudly showing a Tesla avoiding a collision with a person, the car seems to leave it to the very last second and then slams on the brakes, where my mind is already screaming "BRAKE BRAKE BRAKE" a couple of seconds after I would have started a precautionary slowing.

I have no idea how we’re ever going to achieve trustworthy self-driving cars before we achieve full artificial intelligence. I know how I react to different situations is fully contingent on recognizing what I’m actually looking at and being able to generate a risk probability model in real-time.

If I don’t know what I’m looking at, I have to be extremely conservative and if I had to do that every time, my driving would probably be very uncomfortable.

Your question is interesting, as weird things happen even in cities, and not infrequently.

> I'm curious how Tesla FSD works with obstacles that aren't cars or people on roads.

This video would appear to show it the Tesla plowing into a lit up, stationary car. It looks to be dark, but that’s abysmal.

I'm out in the country and often see quails and jackrabbits running across the road (both appear extremely suicidal in darting in front of cars). I can't imagine any auto-pilot being able to deal with that. Now I know the areas where they tend to dart, so precautionary slow driving helps, but would an auto-pilot know that as well?

Forget flashing lights. Why would the car not stop or even slow down when a large immovable object is within impact distance? Auto-brake has been around for a decade now.

They took the radar out , didn’t they?

You make a very good point. I've seen videos from Tesla's AI days where they show debug bounding boxes around identified objects, and the boxes always seem to disappear when objects are occluded. Perhaps that's just a visualisation choice, but I'm taking the precautionary approach of assuming it means that the car has no object permanence because it's not considering a wide enough time window.

They just released an Emergency Vehicle Detection feature on basic autopilot, and so far it works.

This is a big part of the rewrite of autopilot, the ML they use can now persist data through time, so it can reasonably predict if occluded objects will remain in place.

Emergency Vehicle Detection?! This was a stopped vehicle in the middle of the highway, maybe we could start by detecting those?

The crash was in August, well before this feature came out. The FSD rewrite has been fixing the issues with static object detection. Recommend watching Andrej Karpathy's AI Day presentation if you're interested in how and why static object detection is such a challenge.

Slowly moving is much easier to a lot of systems than stationary. An easy way to distinguish a car from the guard rails or other fixed objects is to look at movement, most radar systems do this.

Very true, but I guess from a probabilities perspective, you're highly likely to encounter a stationary car in the driving lane (i.e. a badly parked car). I can't think of a time I've driven and not encountered this, and it's easy to react in advance before it was even a priority (see parked cars ahead, move out a lane, no inconvenience experienced...)

I wonder how it would cope with other items that don't "belong" in the roadway sitting stationary - skips, bins, shipping containers, large piles of bricks or rubble etc.

> An easy way to distinguish a car from the guard rails or other fixed objects is to look at movement

I would prefer not to drive into any of them. Differentiation between types of obstacles is irrelevant in that regard.

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