Comment by jasode | Hacker News Reader

Comment by jasode | Hacker News Reader

>These systems are designed to ignore static obstacles because otherwise, they couldn't work at all.

>But it also detects lots of things a car rolling down the highway needn't worry about, like overhead highway signs, loose hubcaps, or speed limit signs. So engineers make a choice, telling the car to ignore these things and keep its eyes on the other cars on the road: They program the system to focus on the stuff that's moving.

I don't know about the sensors on today's semi-autonomous cars but it seems like there's already enough data there to prioritize collision avoidance over forward motion.

It seems like the Wired story is very incomplete about the details. It needs a more "Ars Technica" in depth treatment or more ideally, an actual Tesla engineer to explain the self-driving computer's decision tree.

How do today's sensors that advertise their ability to "maintain distance" between cars work?

What's the difference between the following to scenarios to the front-mounted sensor?

  - 60mph Tesla slamming into a slow-moving road cleaning vehicle at 20mph

  - 40mph Tesla slamming into a stopped vehicle at 0 mph.

Isn't the ongoing computation of dy/dx to determine a "safe" gap between cars the same?

EDIT to summarize replies with a possible technical explanation...

The resolution of the radar sensor treats overhead signs (like highway signs[1]) as being on the "same 2-dimensional plane" as cars directly in front of you. This would generate false positives. It's not 3D radar which would yield spherical data inputs of impending collisions. Without 3D data, you can't write a rule that ignores objects not 0 degrees in front of the car.

The resolution of radar treats surface level signs (such as the double-arrow[2] at T intersections) as being the same harmless mass as a stationary car. The low resolution cannot distinguish between the shape/footprint of small signs that are supposed to be there vs (large) stationary cars that are not. This object categorization requires LIDAR instead of radar.

Therefore, programming an unambiguous algorithm to prioritize "collision avoidance" is not possible with the current radar sensors. Is that an accurate summary of the technical limitations?

[1] https://www.google.com/search?q=overhead+green+highway+signs...

[2] https://www.google.com/search?q=2+direction+traffic+sign+dou...

The difference is that if all you have is a radar telling you that something is moving at 20mph then you can guess that it's probably a vehicle in the road and you should brake. But if there's something that's stationary at 0 mph it might very well be an overhead sign or otherwise something that you don't have to avoid. A radar can be very precise in detecting distance and relative velocity but very bad at detecting which direction something is in. For the sort of non-dish radars used in cars I'm not even sure they can detect direction at all.

Ignoring things that aren't moving is a standard technique in radar to prevent your returns from being swamped by, e.g., the ground instead of fast moving planes you're looking for.

To add even more detail, as there still appears to be a lot of confusion:

The radar systems in these vehicles send out a radio pulse in a broad approximate-cone forward. They get bounces back from everything that reflects radio in front of them. Distance from the object is calculated by time between pulse and response. Speed towards/away from the object is calculated from Doppler shift of the radio frequency.

There are two main things that these systems can't detect.

1. Speed of the object perpendicular to the direction of radio wave travel.

2. Location of the object within the approximate-cone the radio pulse travels in.

Note that thanks to the second, you can't calculate the first with higher-level object tracking, either.

So the data you get back is a list of (same-direction velocity component, distance) pairs. There's no way to distinguish between stationary objects in the road and stationary objects above the road, to the side of the road, or even the surface of the road itself.

Radar just doesn't provide the directional information necessary to handle obstacle detection safely.

The difference is (according to the article) that there is an abundance of objects at 0mph - signs, litter, barriers - so the system filters all of these out to avoid constantly braking. There is no such abundance of ignorable items going at 20mph.

I don't think any item is "ignorable" if you are about to run directly into it. I get that the computer might not be able to track every road sign or object to the left or right of the car.

Still do you want your car to directly hit a sign, litter or barrier? Why not have a second smaller system like the backup warning sensors most cars have to avoid hitting non-movable objects taller than 6 inches?

> Still do you want your car to directly hit a sign, litter or barrier

If I was at highway speeds and a bag blows in front of me? Yes I want to hit it. Slamming on the breaks might kill me.

But in curves (or directly before them) you have plenty of stationary items that you appear to happily drive into in a few seconds. They're right ahead, after all.

Also note that despite all the hype surrounding those features you're still supposed to pay attention as a driver. Marketing and manuals tell very different stories here and the liability question is purely answered by the latter.

Since the car knows its speed, it may categorize detections by their velocity relative to the ground (i.e. just subtract the car’s velocity from whatever the detection indicates). This would be able to filter e.g. road signs vs cars moving slower regardless of relative velocity as long as the car is moving fast enough.

Humans aren't that great at this either, especially considering our tendency to follow too closely.

I witnessed an accident very similar to this. A vehicle was partially blocking the left lane of a 4 lane highway. A pickup truck was traveling at highway speeds in the left lane. A small 4-door car was following the truck too closely.

The truck saw the lane blockage fairly late and performed an emergency lane change to avoid hitting the stopped vehicle. The small car behind the truck slammed into the stopped vehicle without ever even hitting the brakes. The driver was very seriously injured(the car battery was in her lap and most of the engine had entered the cab) but afaik survived.

That was my life lesson to always allow space between me and the vehicle in front of me.

A great rule - always look ahead of car you're following. If you can't (e.g truck or bus), either give it a fair amount of distance or overtake. If neither is possible, stay super cautious.

Yep. I often try to look 'thru' the windows on the vehicle in front of me to see what's going on in front of it.

Seeing that car plow into the stopped vehicle plays through my head routinely when driving.

No because relative speed between the two vehicles is different then stopped objects.

These two things are treated differently by a radar system - imagine you are driving in semi AV mode and you come around a turn and there is a vehicle on the side of the road stopped (i've been in this situation). For a hot second the car is pointed right at that fixed object and is probably still processing the lane curvature (if it can at all, some adaptive cruise systems don't have lane monitoring). The vehicle will ignore the fixed object and keep going as to not slam on the brakes which can be very dangerous.

It’s an AI using deep learning and therefore magic requiring no explanation.

The first one is a moving object, so it can't be eg. a traffic sign.

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