This Tiny Tesla Update Could Save Your Life

Added: 2026-05-17

[00:00:00]So deploying airbags 70 milliseconds earlier doesn't sound like a revolution.

[00:00:04]It sounds like a little engineering tweak. But the more I thought about this post from Elon Musk and Tesla, the more I realized that Tesla has crossed an important line here. This is one of the first major examples of a consumer product using AI not just to react to reality, but to predict the future in real time. This not only has huge implications for our safety, but can tell us a lot about where Tesla, robotics, and even AI systems in general should be heading. It turns out prediction changes everything. Let's take a look. Before we start, a quick shout out to my channel sponsor Joah.

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[00:00:55]So, be sure to check out Joah. And now, let's get back to it. Hey y'all, it's Dr. Knowit All. This is a post from a few hours ago, 18 hours ago as I record this. This is from Tesla and Elon Musk reposted this. Elon said, "Tesla AI Vision deploys airbags before impact, which greatly reduces risk of injury or death. This comes for free on all new cars and also all of us who own cars get the software updates that they are improved as well." And then Tesla itself said, "Tesla Vision allows us to deploy airbags up to 70 milliseconds earlier.

[00:01:24]If your Tesla detects an unavoidable collision, this could be the difference between serious injury and walking away from a crash. So, you can see here this video is a minute and 13 seconds long.

[00:01:32]We're going to look at it in some detail, but first of all, I want to go ahead and play it.

[00:01:38]70 milliseconds is like a blink of an eye. With the vision system, we're looking at up to 70 milliseconds early airbag deployment decision, but that can be the difference between a serious injury and walking away from an incident.

[00:01:52]We've been able to utilize a new capability of Tesla vision supplementing our existing restraint system. So the way the system works is we've got cameras around the vehicle. So if we had a car accident where two vehicles are coming towards each other, the camera in the Tesla is watching that vehicle and can identify exactly when contact's going to be made and how severe the crash will be. That information [music] is then passed through to the airbag controller. By passing that information across, we can rely on the impact sensors here, here, and here on this vehicle. We're still using impact sensors to [music] detect crashes. We're just supplementing our decisions by using information from the vision system. This is going to roll out as a software update to existing vehicles.

[00:02:32]Tesla customers will wake up tomorrow morning, get a software update, and they'll have this new feature that makes their car significantly safer. This is something outside the bounds of the five-star safety ratings. milliseconds matter. [music] >> So, we're going to get back to these red dots here in a minute because there is another post which is an excellent post as well. But first of all, I want to go back and just examine they don't really show an awful lot of examples of slow versus fast. But here they actually do and we can look at this frame at a time.

[00:03:02]So, I'm going to shift it over just a little bit here. Anyway, you can see on the right that is the fast detection system. Here is the slow detection system. And you will see how much faster the airbag actually deploys in the fast system versus the slow system. So again, we're just going to go frame at a time.

[00:03:16]So you can see already that during the impact, the seat is moving forward. The person is moving towards the steering wheel. All that kind of stuff is happening.

[00:03:24]And during the slow detection system, the airbag finally deploys right about as the person's face actually hits the steering wheel. Right there. Right.

[00:03:34]Okay. So let's back this up. So you're watching on the left hand side. It looks like I don't know if they did this slightly out of sequence with each other or not. Anyway, but you can see it looks like they're starting to come forward.

[00:03:43]Yeah, they're coming forward on the fast one as well, but then the airbag deploys here. So, this is obviously a high-speed camera, but you can see that the airbag deploys where the person's head is still behind this divider between the glass roof and the front windshield, whereas this person is already way their face is already way in front of that and nearly hitting the steering wheel. And of course, they have accelerated rapidly here, too. This is all happening very, very quickly. So as the person is coming forward, they're accelerating towards that steering wheel. Which means that when the airbag deploys, they're accelerating much faster towards that airbag which is just being deployed versus over here where you can clearly see that the airbag is deploying earlier and that the severity of the injury in this case is going to be significantly less because the airbag is already deployed here. The airbag is just deploying here. The person could easily hit their head into the steering wheel or something else bad could happen. You can also see that their knees have come up and they could easily hit the dash area as well, right? So, they're hitting, they're hitting, they're hitting. But over on the right hand side, you can see that with the airbags deploying more rapidly. And also, you can see that the passenger front airbag is deployed rapidly as well. And the passenger, even though you can't see them very well, is also likely to be injured much more severely in the slow case than the passenger in the fast case. And so, here you can actually see the person's head has actually hit the steering wheel while there is no danger of that happening over here for either the driver or the front passenger. And if we continue walking this forward, you can again see that the airbag has fully deployed and that it's cushioning the people on the right hand side significantly better than the left.

[00:05:10]There's not nearly as much of a fling back here. Right? So these airbags have to deploy very very rapidly if there is imminent threat of collision with the person's head against the steering wheel. While here they can deploy more slowly. And by the way, modern airbags can actually deploy at different rates.

[00:05:25]Tesla's safety card actually talks about this. And you can probably tell here that this airbag is able to deploy more slowly and that allows more time for the person to contact the airbag or in this case the crash test dummy. But anyway, it allows the crash test dummy or the human to impact the airbag more slowly.

[00:05:41]There's a lot more pillowing effect here, right? Because it's much more inflated over here than it is here because part of it got crushed through the steering wheel. And continuing on here, just the last few frames, you can see the person on the left here got flung back a lot further. Their head will then impact the seat. I don't even see the passenger over here. I think they went down very very far. But you can see over here on the right in the fast system that both the driver and the passenger were able to be cushioned much better than the people on the right because the airbag deployed a mere 70 milliseconds early.

[00:06:11]So you're probably thinking 70 milliseconds that is not a lot of time.

[00:06:14]Well I looked it up and got some data from Nitsa from National Highway Traffic Safety Administration and an entire high-speed collision can happen on the order of 70 to 150 milliseconds. That's the entire collision. So the whole thing can happen as fast as onetenth of a second or 100 milliseconds or less. And so if the airbags can deploy 70 milliseconds faster, and by the way, it's not just the airbags. It's also the pre-tensioning system in the seat belts as well. They deploy more quickly. They understand what's about to happen. So they can actually keep the person from rocking forward in the seat. And having the seat belt extend a certain distance before it locks up. It can pre-tension those seat belts for the impact prior to the impact actually happening. So anyway, 70 milliseconds actually gives you somewhere between 50% and about 100% more time to deploy the airbags to pre-tension the seat belts in your Tesla versus a traditional car because it sees what's going on in the world prior to it happening. And if we look at Tesla's safety card, and of course I will leave links to all these things in the description so you can look at them.

[00:07:13]There are some details here. This is from May 9th of 2025, so exactly one year ago. But they talk in this document about optimal restraint and airbag deployment based on occupant size position and environmental factors. They also note that they have advanced airbags including active venting that changes airbag behavior dynamically as it's being deployed.

[00:07:33]So that takes us to the red dots. This is from Wes who is Cybertruck lead engineer and reliability testing and analysis for all Tesla vehicles. So thank you for posting this Wes. I'm going to go down here and just play this. It's a little tiny loop. It just goes like 2 seconds here. But basically the gray dots here are severe injuries that happen and the red dots are with the 70 extra milliseconds. And you can see what happens is that most of the severe injuries and crashes go way way way down. You can see that these gray dots remain up here to show you where they used to be, but these red dots are post 70 millisecond upgrade that happens. So what does Wes have to say here? Every one of these dots is an actual crash from the fleet. So this is not hypothetical. This is real. real world speeds, collisions, and people, not just the regulatory test cases. And this is really important because Tesla tests way, way, way outside of Nitsa regulations. They do a lot more than Nitsa requires. The richness of this data is what enabled the result. With simulation, we can replay the crashes and measure the forces on the human body model, then sweep through the restraint deployment times to find that deploying earlier gives the time for the bag to be inflated optimally and seat belt pre-tensioning that we just talked about before the occupant has moved out of position. So, that's critical. the person hasn't begun to shift in their seat and move around. They've already deployed a cocoon around the person. And by the way, that side on view that we saw, there would also be side crash bags that would deploy as well. But of course, the door was removed so we could see the occupants. And then here's the critical part, but it takes time for crash accelerometers to be certain. In other words, once you hit that impact, there are accelerometers inside the vehicle that measure that. But it takes them a while to understand and know that it wasn't just going over a bump or just hitting the brakes extra hard or something like that, but it's a real crash. That's the problem is it takes time for them to make that detection.

[00:09:14]And meanwhile, the car is crumpling, right? You're involved in the crash already. That means that they're reactive, not pre-active. Anyway, Wes says it takes time for the crash accelerometers to be certain. Lowering that time threshold risks unwanted deployments. In other words, you don't want to go over a big pothole or something like that and have your airbags deployed. That would be a very very bad day. Using vision gives the vehicle confidence to reduce that timing. The cameras, in other words, the ones you use for full self-driving that are looking around 360° all the time.

[00:09:42]They see the impending impact and together with the sensors tell the restraint controller to reduce the filter and act sooner. The Y-axis shift and predicted injury severity. In other words, the red dots that are going down is based on sensors in the human body models from rerunning the crash simulations with the faster detection threshold. Such a reduction in injury severity across the spectrum is unheard of, let alone doing this via an update over the air. And as I understand it, everybody received this in the last Tesla update or you will be receiving it very, very soon. So that is really amazing that our cars got safer over the air. That's such a huge difference between a Tesla and any other vehicle.

[00:10:17]And people who care about safety but purchase other vehicles, I just don't know why you do that because Teslas are demonstrabably safer. You can literally see that in the crash test information that they posted in that video. Anyway, Wes concludes, "I'm extremely proud of the analysis team's work and dedication going above and beyond to ask, we have the safest car on the road, but can we make it even safer?" That is always a good question to ask. And then working with the vision team, which would be Tesla AI, to build the predictions necessary to make it happen. Rigorously tested in simulation and then in real physical crash testing. Now deployed and improving lives. I watched this video on loop and just imagine each dot a person.

[00:10:54]And that's very, very cool because even though Teslas are amazingly safe and if you drive on full self-driving, you can go millions of miles without getting involved in an accident. If you haven't seen that video, watch it here. I'll leave it at the end of this video. It's well worth watching to see that because that also plays into it. The best accident is no accident, obviously. But if an accident is inevitable, it will happen, then lowering the severity of the crash is critical. And you can see from this video right here that the severity of the crashes is being lowered.

[00:11:22]So, let's finish this off by expanding our point of view just a little bit. Why does this matter for Optimus and robotics in general? Well, even more than a four-w wheeled robot, a humanoid, four-lemed robot fundamentally needs predictive balance, predictive force estimation, predictive collision avoidance, and predictive planning.

[00:11:39]Those are all predictive kind of things.

[00:11:41]They're not reactive. So therefore, the same architecture that Tesla's crash team is working on here can be utilized in Optimus, not just to avoid and mitigate crashes, but also to predict reality in general significantly better.

[00:11:54]And you can notice this kind of thing because of course reactive looks clumsy.

[00:11:57]If you've ever tripped over something while you were walking, you look kind of clumsy for a few seconds there because you're reacting and trying to catch your balance or you fell flat on your face, whatever it is. much much better to be predictive and just avoid it by stepping your foot up a little bit and avoiding that tree route or whatever it is that trips you. That kind of a thing is much less clumsy. It looks more elegant. And of course, that predictiveness is inherent in human beings. And so, a robot that acts that way is also going to look more human and more alive. So, my prediction here is that Tesla is not only using this for their cars, but they're using it for their Optimus robots as well. And then finally, we can turn to AI systems. Overall, large AI systems are increasingly becoming prediction engines. They do next token prediction. They do world models that are predictive. They run autonomous agents that need to be able to simulate future states. And of course, orchestration agents, the one at the top, need to do predictive planning for the entire system. So, this doesn't just matter for a Tesla vehicle in an accident. This could scale all the way up to the largest large language models and world models. They need to be able to predict language, physics, human intent, that's very important. Traffic, the environment, and the consequences of their actions. All of these things matter. So that's why when I saw this Tesla post, I was like, "Holy mackerel, this really actually matters a lot."

[00:13:09]What it represents here is a subtle but important transition. We're looking at machines moving away from reacting to the world to anticipating and predicting it. And once robots begin to reliably predict reality before it unfolds, everything changes and for the better.

[00:13:24]All righty, folks. That's what I've got for you today. Let me know in the comments what you think about all of this. I think it's a really big deal.

[00:13:29]Hopefully, you do as well after going through all of this with me. Let me know in the comments what you think. While you're down there, if you don't mind liking the video, it really helps out.

[00:13:36]And if you want to help out the channel even more and help us get to 100,000 subscribers in 2026, please do consider subscribing and hitting that bell notification icon. And I will see you in the next video. [music] Bye-bye.

[00:13:51]>> [music]