[HN Gopher] A Short Introduction to Automotive Lidar Technology
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A Short Introduction to Automotive Lidar Technology
Author : kayson
Score : 46 points
Date : 2024-11-25 20:12 UTC (2 hours ago)
(HTM) web link (www.viksnewsletter.com)
(TXT) w3m dump (www.viksnewsletter.com)
| kayson wrote:
| Related: https://www.viksnewsletter.com/p/teslas-big-bet-cameras-
| over...
| Animats wrote:
| Waymo tried cameras-only recently as a research project.[1][2]
| They seem to do about as well as Tesla, which they don't
| consider good enough.
|
| [1]
| https://www.forbes.com/sites/bradtempleton/2024/10/30/waymo-...
|
| [2] https://arxiv.org/pdf/2410.23262
| xnx wrote:
| One of the cool thing about the Waymo Driver is that it can
| be configured to work with different degrees of quality
| depending on the sensors available. In a low risk environment
| (e.g. closed to humans) like operating forklifts in an
| autonomous warehouse, it would work fine with just cameras.
| Waymo hasn't been very boastful to date, but some of the
| capabilities are hinted at in this interview:
| https://www.youtube.com/watch?v=d6RndtrwJKE
| MaxPock wrote:
| Fantastic tech that Musk hates
| quonn wrote:
| It's not just Musk. Most automobile manufacturers have
| maintained that they need to find a way to do it with cheap and
| pretty sensors.
| juliushuijnk wrote:
| > have maintained that they need to find a way to do it with
| cheap
|
| If the goal is to make roads safer. Aiming for cheap is good,
| it means aiming for more people who can afford that safer
| car. If it's not safer than humans, it should not be on the
| road in the first place.
| r17n wrote:
| So there's a video of him addressing this - he doesn't hate the
| tech. He mentions that it's wildly expensive for cars. But,
| they use it heavily for SpaceX
| threeseed wrote:
| The issue isn't that it's wildly expensive for cars. But
| rather for Tesla.
|
| Because the company has promised that existing Tesla owners
| would be able to use FSD.
|
| Having to retrofit them to add LiDAR sensors would be cost-
| prohibitive.
| stormfather wrote:
| Also he wants to reuse the foundational machine vision tech
| in Optimus bot, which probably won't have lidar.
| threeseed wrote:
| Based on presentations we've seen what sets Tesla apart
| are its datasets not the core technology.
|
| And those don't translate across to the Optimus bot.
| peppertree wrote:
| I hate to say this but Musk was right. We already have billions
| of RGB photos that can help models understand the world. Lidar
| just doesn't have the same kind of training data. RGB sensors
| are just going pull further ahead as teams start using large
| foundation models to simulate ground truth.
| rightbyte wrote:
| "Its particular superpower is that it can generate high
| resolution images of its surroundings much better than radar
| can."
|
| Is this true tough? Car radars are fixed. I guess a comparable
| lidar would be fixed too and have n points for n lasers.
|
| A rovolving radar would have continuous resolution around while a
| lidar samples?
|
| I thought the advantage of lidars were accuracy and being better
| at measuring heights of objects, where as radars flatten the
| view.
| lupusreal wrote:
| Very high tech radars can generate amazing imagery, but they'll
| never top what lidar can do. Conceptually they're both doing
| the same sort of thing using EM radiation, but lidar uses a
| _much_ smaller wavelength which gives it an intrinsic
| resolution advantage. Particularly at distances and with
| hardware sized relevant to cars.
| ender7 wrote:
| The issue isn't one of fixed vs rotation, it's that radar can't
| fundamentally achieve the resolution necessary to distinguish
| important features in the environment. It's easily fooled by
| oddly-shaped objects, especially concave features like corners,
| and so while it's great for answer the question of "am I close
| to something" it's not reliable for telling you what that
| something is, especially at longer ranges.
| xnx wrote:
| I believe automotive radar has a cone of sensitivity that is
| read as a single "pixel" worth of data. Even if the radar spun
| like lidar, the radar cone of sensitivity is thousands of times
| wider than the lidar beam so you can't make much of a picture
| with radar.
| Animats wrote:
| That's a reasonable basic overview.
|
| I'm surprised that rotating scanners are still used. It's been
| twenty years since Velodyne built their first one. They work OK,
| but cost too much. I was expecting flash LIDAR or MEMS mirrors to
| take over. Continental, the auto parts company, bought the
| leading flash LIDAR company over a decade ago, but the volume
| market a big parts company needs never appeared.
|
| Waymo is still using rotating LIDARs even for the little ones at
| the vehicle corners. Those need less range. There needs to be a
| cheap, flush-mounted replacement for those things. The location
| is too vulnerable. Maybe millimeter phased array radar mounted
| behind Fiberglas body panels. Waymo needs to solve that problem
| before they do New York.
|
| The LIDAR on top may not be a problem. Insisting that it has to
| go away to "look like a car" is like insisting that cars had to
| have the form factor of horse-propelled buggies. Early cars
| looked like buggies, but that didn't last.
|
| One big advantage of pulsed LIDAR over continuous is that the
| interference problem between identical units is much less. The
| duty cycle is tiny. Data from one pulse round trip is collected
| in less than a microsecond. Just put some randomization in the
| pulse timing and getting multiple conflicts in a row goes away.
| xnx wrote:
| > They work OK, but cost too much.
|
| Costs have dropped dramatically in the past 20 years and
| continue to do so.
|
| > There needs to be a cheap, flush-mounted replacement for
| those things.
|
| Why? Corners are the optimal mounting position for maximum
| visibility. It allows the car to -in-effect- see around corners
| in ways no centrally mounted sensor can.
|
| > Waymo needs to solve that problem before they do New York.
|
| What? Because of vandalism?
| aftbit wrote:
| Have you ever seen the corners of a car that has been parked
| in a big East-coast city? They will sustain damage during the
| course of normal operation and storage, and many people will
| not stop and leave their insurance information, especially if
| the damage is perceived as minor and happens while the car is
| parked and the owner not present. Currently, the corners of a
| car are relatively non-critical to its function and usually
| not too expensive to repair. If both of those change, we'll
| see more expensive damage that is more challenging to repair
| as well as less likely to be handled by the responsible
| party.
|
| Also, having the sensors stick out from the corners makes the
| car's collision box and turning radius bigger. That doesn't
| help in any tight situation, but I imagine that's not that
| different between e.g. SF and New York. What is different is
| the sheer volume of cars and pedestrian activity.
| atomic128 wrote:
| Here's an interesting "lidar gem" from Hacker News a few years
| ago:
|
| https://news.ycombinator.com/item?id=33554679
|
| _Lidar obstacle detection algorithm from a Git repo leaked onto
| Tor_
|
| _This is a drivable region mapping (obstacle detection)
| algorithm found in what appears to be a git repo leaked from an
| autonomous vehicle company in 2017. The repo was available
| through one or more Tor hidden services for several years._
|
| _The lidar code appears to be written for the Velodyne HDL-32E.
| It operates in a series of stages, each stage refining the output
| of the previous stage. This algorithm is in the second stage. It
| is the primary obstacle detection method, with the other methods
| making only small improvements._
|
| _The leaked code uses a column-major matrix of points and it
| explicitly handles NaNs (the no-return points). We 've rewritten
| it to use a much more cache-efficient row-major matrix layout and
| a conditional that will ignore the NaN points without explicit
| testing._
|
| _This is an amazingly effective method of obstacle detection,
| considering its simplicity._
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