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Why Tesla removed radar and ultrasonic sensors [video]

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Re: Why Tesla removed radar and ultrasonic sensors [video]

#471

Post by leetharris »

RC_ITR wrote: post #208
stefan_ wrote: post #141

Earlier quoted context omitted.

This thread is unhelpfully mixing radar and ultrasonic sensors. Ultrasonic sensors, as your video explains, are primarily used as a parking aid; they are tuned for too low a distance to be helpful in just about any kind of driving scenario at speed. Meanwhile, radar is the principal sensor used in systems like automatic emergency braking across the industry. It has no intersection with any of the parking stuff becaus…

The first famous autopilot crash was because a white semi-truck was washed out by the sun and confused for an overhead sign. That's literally trivial for a car with radar to detect. Amazing how people talk about stuff they have no idea about when it comes to Tesla.

What a smug post from someone who is completely wrong.

The car that crashed had radar, vision, USS, AND it was based on another company's technology.

Re: Why Tesla removed radar and ultrasonic sensors [video]

#472

Post by kennyloginz »

itsoktocry wrote: post #406
hammock wrote: post #395

Earlier quoted context omitted.

Devils advocate, if the cost of working to improve cameras to the point where they eliminate that delta is lower than the cost of using the sensors instead, then it is a net benefit

> f the cost of working to improve cameras to the point where they eliminate that delta is lower than the cost of using the sensors instead In a vacuum, how can cameras ever be better than cameras + other sensors?

All forms of transportation could be made safer , if inside a vacuum and ignoring the economic reality ( cough, Boeing ).

Re: Why Tesla removed radar and ultrasonic sensors [video]

#474

Post by 01100011 »

snovv_crash wrote: post #296
01100011 wrote: post #69

Earlier quoted context omitted.

You are confused my friend. Reading sensor data is not the same as feeding that data to a neural network and asking it to form a worldview composed of possibly conflicting sensor data streams(i.e. lidar vs vision vs ultrasonic). You are somewhat correct that it is quite trivial to read sensor data. For many sensors, there is some work which needs to be done to denoise or cleanup the input data. That's not where the s…

In order to display the gravity-aligned acceleration, sensor fusion between gyro and accelerometer has to occur. This is typically done with a Kalman filter, and runs on 1960s levels of hardware. If you look at something like a drone autopilot, eg. Ardupilot, the sensor fusion is soo cheap that they even extended the Kalman filter to also estimate things like the sensor bias offsets and earth's magnetic field vectors…

We're talking about radar and ultrasonic sensors here, not accelerometers. We're also talking about feeding them to a deep neural network. Not the same thing. Sensor fusion is not being done with a Kalman filter in this case.

Re: Why Tesla removed radar and ultrasonic sensors [video]

#475

Post by 01100011 »

vhold wrote: post #299
01100011 wrote: post #19

I still suspect it's because they need to preserve compute resources for vision processing. Sensor fusion is likely eating up too much of their current HW and limiting their progress in other areas. I suspect Tesla will have to admit they need to upgrade the current HW before they ever 'solve' FSD.

One camera produces millions of bytes of data every single frame, an ultrasonic sensor is useful producing just 1 or 2 bytes of data in the same time span. (distance to something within the sensor's cone). So it seems like a totally ridiculous argument that ultrasonic sensors create some kind of data processing overload. An ultrasonic sensor makes it possible to implement incredibly simple and reliable safety feature…

Not saying they create a data processing overload. I'm saying they're fed to a deep learning architecture that must then try to fuse disparate sets of data into a coherent picture. The neural network becomes simpler when you remove that function and just focus on visual processing.

Re: Why Tesla removed radar and ultrasonic sensors [video]

#476

Post by fennecfoxy »

Reason077 wrote: post #257
RC_ITR wrote: post #248

Earlier quoted context omitted.

So, yes, cars that are programmed to have AEB: perform well at AEB and not other tasks. We are in agreement here. (I even agree with you that those cars use Radar for AEB). Now, where we disagree is you implying that cars with AEB-level radar (literally $10 off-the-shelf parts with whatever sensor fusion some MobilEye intern dreams ups) are somehow the same as self-driving cars (the goal of Tesla Autopilot). Every se…

> ”There is no one sensor that will do it. Full stop.” Yet somehow, humans can drive cars with just a pair of optical sensors (mounted on a swivelling gimbal, of sorts). In theory, a sufficiently capable AI should be able to drive a car at least as well as a human can using the same input: vision.

I believe in the sentiment, but it's true that we humans also crash our cars a LOT. From minor bumps and scrapes to multiple fatalities.

Re: Why Tesla removed radar and ultrasonic sensors [video]

#478

Post by funstuff007 »

jholman wrote: post #453
funstuff007 wrote: post #380

Earlier quoted context omitted.

> because it's often true that you can deliver better by focusing on a smaller number of things. This is true / dogma in linear / non-linear regression world, but of no real import in deep learning or Bayesian methods.

Sorry, it's your opinion that researchers and/or engineers working on DL or Bayesian methods work better when they're distracted by many diverse tasks? What?

No, it's my opinion that in linear regression an inordinate amount of time is spent with feature selection and ensure there's no correlations among the features. When data is cheap in both X and Y, winnowing down X is a lot of work.

Re: Why Tesla removed radar and ultrasonic sensors [video]

#479

Post by snovv_crash »

01100011 wrote: post #474
snovv_crash wrote: post #296

Earlier quoted context omitted.

In order to display the gravity-aligned acceleration, sensor fusion between gyro and accelerometer has to occur. This is typically done with a Kalman filter, and runs on 1960s levels of hardware. If you look at something like a drone autopilot, eg. Ardupilot, the sensor fusion is soo cheap that they even extended the Kalman filter to also estimate things like the sensor bias offsets and earth's magnetic field vectors…

We're talking about radar and ultrasonic sensors here, not accelerometers. We're also talking about feeding them to a deep neural network. Not the same thing. Sensor fusion is not being done with a Kalman filter in this case.

Radar and ultrasound both give drastically less data than a simple 720p webcam. After postprocessing their output bandwidth is more similar to a 9-axis IMU than a camera.

Re: Why Tesla removed radar and ultrasonic sensors [video]

#480

Post by snovv_crash »

Nomentatus wrote: post #60
snovv_crash wrote: post #25

Earlier quoted context omitted.

The amount of compute that sensor fusion uses is miniscule compared to running a NN or computing stereo depth maps. Sensor fusion runs in the background of your phone the whole time to power things like [0] for example. 0. https://sensor-js.xyz/demo.html

Well, yes and no. Integrating the data and adjudicating conflicts between sensors is a real task, too. Also having just two opinions doesn't necessarily help if they conflict, and the lidar is the thinnest source of data. How do you coin flip that? You likely end up just discarding the Lidar's conflicting opinion.

LIDAR data might be more sparse but it is also more reliable, being an active sensor so it isn't affected by night.

The real way is to use the LIDAR to add to the depth probability distribution in the stereo depth estimation. This way you aren't throwing any data away. LIDAR often gives probabilities as well, for example, and this can be used to eliminate reflections.

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