Adding a comment here with some info on LIDAR human safety, since many are asking.
There are two wavelengths of interest used:
The failure mode of these LIDARs can be akin to a weapon. A stuck mirror or frozen phased array turns into a continuous-wave pencil beam. A 1550 nm LIDAR leaking 1W continuous will raise corneal temperature >5C in 100ms. The threshold for cataract creation is only 4C rise in temp. A 905 nm Class 1 system stuck in one pixel gives 10 mW continuous on retina, capable of creating a lesion in 250ms or less.a) 905 nm/940 nm (roof and bumpers): 70–100 µJ per pulse max, regulated by IEC 60825 since this WL is focused on the retina b) 1550 nm systems (the Laser Bear Honeycomb): 8–12 mJ per pulse allowed (100x more photons since this WL stays the cornea)20 cars at an intersection = 20 overlapping scanners, meaning even if each meets single-device Class 1, linear addition could offer your retina a 20x dose enough to push into Class 3B territory. The current regs (IEC 60825-1:2014) assume single-source exposure. There is no standard for multi-source, multi-axis, moving-platform overlay.
Additionally, no LIDAR manufacturer publishes beam-failure shutoff latency. Most are >50ms, which can be long enough for permanent injury
I live in town, and walk past countless stopped and moving cars every day.
I also know how the tech industry makes decisions about safety and responsibility (hahaha...). And I have seen some of the recent changes that automakers have somehow slipped past safety regulators. So it seems foolish to trust any of them on this safety issue.
Do we all have to move to rural areas, if we want to be able to go outside without wearing laser safety goggles?
The article talks about eye safety a bit in section 4.
> a stuck mirror
This is one of the advantages of using an array of low power lasers rather than steering a single high power laser. The array physically doesn't have a failure mode where the power gets concentrated in a single direction. Anyway, theoretically, you would hope that class 1 eye-safe lidars should be eye safe even at point blank range, meaning that even if the beam gets stuck pointing into your eye, it would still be more or less safe.
> 20 cars at an intersection = 20 overlapping scanners, meaning even if each meets single-device Class 1, linear addition could offer your retina a 20x dose enough to push into Class 3B territory.
In the article, I point out a small nuance: If you have many lidars around, the beams from each 905 nm lidar will be focused to a different spot on your retina, and you are no worse off than if there was a single lidar. But if there are many 1550 nm lidars around, their beams will have a cumulative effect at heating up your cornea, potentially exceeding the safety threshold.
Also, if a lidar is eye-safe at point blank range, when you have multiple cars tens of meters away, laser beam divergence already starts to reduce the intensity, not to mention that when the lidars are scanning properly, the probability of all of them pointing in the same spot is almost impossible.
By the way, the Waymo Laser Bear Honeycomb is the bumper lidar (940 nm iirc) and not the big 1550 nm unit that was on the Chrysler Pacificas. The newer Jaguar I-Pace cars don't have the 1550 nm lidar at all but have a much bigger and higher performance spinning lidar.
> > a stuck mirror
Detect the mirror being stuck and shut the beam off. Easy.
Hint: how bad would it be if the MCU in your gas heating boiler latched up and wouldn't shut the burner off? How is this mitigated?
This was addressed in the original comment:
> Additionally, no LIDAR manufacturer publishes beam-failure shutoff latency. Most are >50ms, which can be long enough for permanent injury
Yeah, I was surprised stories of Volvo's EX90 lidar damaging camera sensors didn't get more traction: https://www.thedrive.com/news/volvo-ex90s-lidar-sensor-will-...
One would hope there would be more regulation around this.
A quick note about units -- you correctly quote the limits as an energy-per-pulse limit. The theory behind this is that pulses are short enough that rotation during a pulse is negligible, so they tend to hit a single point (on the retina, at focusable frequencies; the cornea itself for longer wave lengths), and the absorption of that energy is what causes damage. But LiDAR range is determined not by energy per pulse, but by power. This drives a desire for minimum-time pulses, often < 10 ns -- if you can halve your pulse length, you can increase your range substantially while still being eye-safe. GaNFETs are one of the enabling technologies for pulsed lidar, since they're really the only way out there to steer tens of amps in single-digit nanoseconds. Even once you've solved generating short pulses, though, you still need to interpret short responses. Which drives either a need for very fast ADCs (gigasample+), or TDCs, which are themselves fascinating components.
I was always curious about this, it's impossible to find any safety certifications or details about the lidars used by e.g. Waymo. Are we supposed to just trust that they didn't cut corners, especially given the financial incentives to convince people that lidar is necessary (because there's a notable competitor that doesn't use it).
To date most class-1 lasers have also been hidden/enclosed I think (and there is class 1M for limited medical use), so I'm not convinced that the limits for long-term daily exposure have been properly studied.
Until I see 3rd party studies otherwise, I plan to treat vehicle lidar no different than laser pointers and avoid looking directly at them. If/when cars become common enough that this is too hard to do, maybe I'll purchase NIR blocking glasses (though most ones I found have an ugly green tint, I wonder if it's possible to make the frequency cutoff sharp enough that it doesn't filter out visible reds).
Every day dozens of Waymos are in close proximity to the people cleaning them and plugging them in, and they are maneuvering in tight spaces amongst other Waymos. That's not a peer reviewed study, but it seems to work.
The fried iPhone pixels are spooky. Eyes clearly aren't as affected, but they also aren't as easy to replace.
Workplace injuries have never been swept under the rug, especially if those cleaners are non-persons in the eyes of the government.
> There are two wavelengths of interest used
Ouster uses (or at least used to use, not sure if they still do) 840 nm. Much higher quantum efficiency for standard silicon receivers, without having to play games with stressed silicon and stuff; but also much better focusing by the retina, so lower power permitted.
Enormous complexity, safety risks, and completely unnecessary for successful level 4 FSD - the hurdle to full autonomous driving was basically jumped by Tesla this year. I don't see why lidar is even allowed in public at this point, it seems dangerous enough that you'd want it effectively restricted to highly regulated and licensed uses, like military or academic scanning, with all sorts of deliberate safeguards and liability checks.
Social media is full of little clips of lidar systems burning out camera pixels, and I'm sure big proponents of the tech have paid people off over eye injuries at this point. There've probably been a ton of injuries that just got written off as random environmental hazards, "must have looked at the sun" etc.
It's nuts that this stuff gets deployed.
Tesla doesn't have driverless operations anywhere, and their Austin fleet consists of <30 vehicles with full time safety drivers that have a far worse safety record than Waymo vehicles.the hurdle to full autonomous driving was basically jumped by Tesla this year.It's not nothing, but it's a long way from being a complete system (let alone the obviously superior one).
IIRC, Tesla's safety record is about 30% worse than Waymo. The gap has been closing rapidly. It's not that long time ago Tesla made an order of magnitude more mistakes than Waymo.
That's with safety drivers, a small fleet, and literally only the most recent data (since it wasn't broken out before). My experience with AV deployments is that your incident rate is significantly different once you remove humans, and small fleet sizes/deployment areas hide a lot of long tail issues.
Waymo is operating at a much larger scale across a huge range of conditions with hardware that's generations behind their latest and still performing better.
Ok, I guess we'll need to wait until Tesla removes the safety drivers to see the whole truth.
If that was true they would've already released a full, no safety driver service. They have not.
Having built a LiDAR system for an autonomy company in the past, this is a great write-up, but it omits what I found to be one of the more interesting challenges. For our system (bistatic, discrete edge-emitting laser diodes and APDs; much like a Velodyne system at high level), we had about an inch of separation between our laser diodes and our photodiodes. With 70 A peak currents through the laser diodes. And nanoamp sensitivity in the photodiodes. EMI is... interesting. Many similar lidars ignore the problem by blanking out responses very close to firing time, giving a minimum range sensitivity, and by waiting for maximum delay to elapse before firing the next salvo -- but this gives a maximum fire rate that can be an issue. For example, a 32 channel system running at 20 kHz/channel would be limited to ~200 m range (468 m round trip delay, some blanking time needed)... so to get both high rate (horizontal resolution) and high channel count (vertical resolution), you need to be able to ignore your own cross-talk and be able to fire when beams are in flight.
200m range seems adequate for passenger vehicle use. Even at 100kph that's over 7 seconds to cover the distance even if you aren't trying to slow down. I think there is diminishing returns with chasing even longer ranges. Even fully loaded trucks are expected to stop in about 160m or so.
Yep, 200 m is pretty close to standard. Which is why 32 channel and 20 kHz is a pretty common design point. But customers would love 64 channel and 40 kHz, for example. Also, it's worth noting that if your design range is 200 m -- your beam doesn't just magically stop beyond that. While the inverse square law is on your side in preventing a 250 m target from interfering with the next pulse, a retro-reflector at 250 m can absolutely provide a signal that aliases with a ~16 m signal (assuming 234 m time between pulses) on the next channel under the right conditions. This is an edge case -- but it's one that's observable under steady-state conditions, it's not just a single pulse that gets misinterpreted.
>we had about an inch of separation between our laser diodes and our photodiodes
Why can't you place them further away from each other using an additional optical system (i.e. a mirror) and adjusting for the additional distance in software?
You can, but customers like compact self-contained units. All trade offs.
Edit: There's basically three approaches to this problem that I'm aware of. Number one is to push the cross-talk below the noise floor -- your suggestion helps with this. Number two is to do noise cancellation by measuring your cross-talk and deleting it from the signal. Number three is to make the cross-talk signal distinct from a real reflection (e.g. by modulating the pulses so that there's low correlation between an in-flight pulse and a being-fired pulse). In practice, all three work nicely together; getting the cross-talk noise below saturation allows cancellation to leave the signal in place, and reduced correlation means that the imperfections of the cancellation still get cleaned up later in the pipeline.
Some correction here. FMCW lidar does not need fiber lasers. In fact most fiber lasers are actually very difficult to frequency sweep internally. Typical lasers used in swept wavelength interferometry (which is really the same thing) are so-called external cavity lasers, which rely on photodiodes + external cavity e.g. through a wavelength selective feedback (still comparably expensive though).
Baraja selling point was AFAIK that they used a integrated swept laser source (they typically have lower coherence but you can work around that in DSP).
In the current state of self-driving tech, lidar is clearly the most effective and safest option. Yet companies like Tesla refuse to integrate lidar, preferring to rely solely on cameras. This is partially to keep costs down. But this means the Tesla self-driving isn't quite as good as Waymo, which sits pretty comfortably at level 4 autonomy.
But humans have no lidar technology. We rely almost solely on sight for driving (and a tiny bit on sound I guess). Hence in principle it should be possible for cars to do so too. My question is this: at what point, if at all, will self-driving get good enough to make automotive lidar redundant? Or will it always be able to make the self-driving 1% better than just cameras?
Let's just do a quick comparison: the visual cortex consumes about 10x more volume of the human brain than the language center. So... that's a rough comparison of difficulty. I seem to remember the visual centers is also a lot older, evolutionarily than the language centers?
There are unquestionably some cases where Lidar adds actual data that cameras can't see and is relevant to driving accuracy. So the real question is whether there are cases where Lidar actually hurts. I think that is possible but unlikely to be the case.
if cameras end up only slightly better than humans - who cause 40k deaths annually and 1M worldwide, or a world war amount of deaths every 15 years or so - but rapidly deployable due to cost, they will save more lives than a handful of lidar cars.
As far as Tesla, time will tell. I ride their robotaxis daily and see them performing better than Waymo, but it's obviously meaningless until we see accident stats after they remove safety monitors.
> I ride their robotaxis daily and see them performing better than Waymo, but it's obviously meaningless until we see accident stats after they remove safety monitors.
I've seen this claimed a lot but never have gotten a definitive answer.
Is this like "overall better but hard to pinpoint" or "this maneuver is smoother than Waymo" or something in between?
Would love to hear experiences with them since they're so limited currently.
Smoother maneuvers, or things like seamlessly backing up a bit when it predicts that a large vehicle turning from an intersecting street won't have enough room to turn unless the car moves out of its way. It's really cool.
Yeah Tesla has more smoothing, but IMO that's less interesting than the ability to navigate tricky scenarios and model other actors. Here's my collection of interesting videos, Tesla only because those are the ones I get forwarded to me. I'd love to see a similar collection for Waymo.
Crowd: https://www.youtube.com/watch?v=3DWz1TD-VZg
Negotiation: https://www.youtube.com/shorts/NxloAweI6nU
> We rely almost solely on sight for driving (and a tiny bit on sound I guess).
And proprioception. If I'm driving in snowy conditions, I'm definitely paying attention to whether the wheels are slipping, the car is sliding, the steering wheel suddenly feels slack, etc. combined with memorized knowledge of the road.
However, that's ... not great. It requires a lot of active engagement from the driver and gets tiring fast.
Self-driving can be way better than this.
GPS with dead reckoning tells the car exactly where it is relative to a memorized maps of the road--it won't miss a curve in a whiteout condition because it doesn't need to see the curve--that's a really big deal and gets you huge improvements over humans. Radar/lidar will detect a stopped car in front of you long before your sight will. And a computer system won't get tired after driving in stressful conditions for a half hour. etc.
> My question is this: at what point, if at all, will self-driving get good enough to make automotive lidar redundant?
By 2018, if you listen to certain circa-2015 full self-driving technologists.
Many humans do a really bad job at driving, so I'm not sure we should try to emulate that.
And it is certain that in India they use sound sound for echolocation.
> Many humans do a really bad job at driving, so I'm not sure we should try to emulate that
Agreed, but there are still really good human drivers, who still operate on sight alone. It's more about the upper bound, not the human average, that can be achieved with only sight.
That upper bound can be pretty low in bad lighting conditions. If you have no strategy to work around that, your performance is going to be bad compared to vehicles with radar and lidar. On top of all that, Waymo's performance advantage might come in part from the staggering amount of geospatial data available to Waymo vehicles and unique to Waymo's parent company.
The second and third place companies in terms of the number of deployed robotaxis are both subsidiaries of large Chinese Internet platforms, and both of them are also leaders in providing geospatial data and navigation in China. Neither operates camera-only vehicles.
No mention of flash LIDAR, which really ought to be seen more for the short-range units for side and rear views.
Interference between LIDARs can be a problem, mostly with the continuous-wave emitters. Pulsed emitters are unlikely to collide in time, especially if you put some random jitter in the pulse timing to prevent it. The radar people figured this out decades ago.
A flash lidar is simply a 2D array of detectors plus a light source that's not imaged. It's mentioned super briefly at the start of section 3 but you're right, I should have gone into more detail given how common and important they are.
For pulsed emitters, indeed adding random jitter in the timing would avoid the problem of multiple lidars being synced up and firing at the same time. For some SPAD sensors, it's common to emit a train of multiple pulses to make a single measurement. Adding random jitter between them is a known and useful trick to mitigate interference. But in fact it isn't super accurate to say that interference is a problem for continuous-wave emitters either. Coherent FMCW lidar are typically quite robust against interference by, say, using randomized chirp patterns.
I am surprised that I didn’t see discussion about Audi’s lidar that’s been in use in production vehicles now. Yes, it’s on a different level, only used for ADAS, but it’s still lidar that’s actively used.
If I remember correctly, the Valeo Scala that's in the Audi cars uses a discrete 16 element 1D array (940 nm diodes + APDs) plus a rotating mirror to scan.
I learned a lot from this article. The breakdown of the different LiDAR types and how they fit into real automotive sensor stacks was especially helpful. Nice to see a clear explanation without the usual hype or ideology around cameras vs. LiDAR.
The discrete array, must be accurate for them to be close like that and not get overlap (eg. receiver 1 gets beam from emitter 2)