Tesla's Track Mode gives the driver even more control over their vehicle by giving them the ability to adjust various settings.
Track Mode in 2021.44.30
JbTeslaman/Twitter (Edited by Not a Tesla App)
The driver can adjust the handling, traction control, and regenerative braking of the vehicle. These settings can improve the handling and acceleration of the vehicle when turning corners.
With Track Mode drivers can also modify the balance of the car to favor a full rear-wheel-drive layout.
In addition to being able to adjust these settings, Track Mode also shows additional visualizations. For example, there’s a g-force graph that allows the driver to see the amount of g-force when turning corners, and you can also see the temperature of the tires, motors and the battery pack.
G-force meter in a Model 3 Performance
Until now, only Performance or Plaid models have been able to take advantage of Track Mode, but this could soon change.
Track Mode was originally designed and calibrated for the Performance Model 3 which is equipped with performance brakes and tires. It is still not available for the Performance Model Y despite being announced last July. With the recent software update 2021.44.30, Tesla rolled out Track Mode to the Model S Plaid, so it’s possible the Model Y may be next.
In addition to customizing how the vehicle handles, the increased performance cooling, and the visualizations, Track Mode also includes some additional features.
Track Mode will let you set start and finish markers on the map. When these markers are set, the car will automatically display lap timings and record each lap through the car's cameras.
The cameras used during track mode are the same ones that are available when using the Dashcam. However, when using Track Mode, the car will automatically save each lap as a separate video to your USB drive.
According to Elon, we will see Track Mode become available for non-performance versions of the Model 3 and Model Y.
In a recent Twitter conversation between @_bennettm and Tesla CEO, Elon Musk, it was revealed that Track Mode will be available for non-performance models. When asked if Model 3/Y owners with Acceleration Boost upgrade could get Track Mode, Musk responded "Ok".
While Track Mode is said to come to non-Performance models with the Acceleration Boost upgrade, it may soon be available in some capacity for all models. Over-the-air performance upgrades are nothing new for the automaker. Tesla launched a $2,000 Acceleration Boost upgrade for some select vehicles last year.
Track Mode on Model 3
Owners with Acceleration Boost, and maybe even those without it can expect Track Mode to become available in a software update in the near future. It's also a good possibility that Tesla may release Track Mode for the Model Y at the same time.
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As Tesla pushes the boundaries of autonomous driving with each iterative FSD update, the hardware that powers every vehicle also needs to continue evolving. With FSD V13 already pushing the capabilities of today’s AI4 hardware, Tesla is actively looking to update its FSD hardware.
Korean news outlet MK (Korean) has provided what seems to be a credible glimpse into Tesla’s next hardware iteration, AI5, and what it could be capable of. MK’s report claims that Tesla is preparing for the production of its new AI5 FSD computer with a performance target of 2,000 to 2,500 TOPS (Trillion Operations Per Second). According to the report, Tesla is considering using Samsung and TSMC to manufacture the hardware.
Putting the Compute Power into Perspective
To grasp what exactly that 2,500 TOPS number means, let’s compare it to Nvidia’s recently released gaming GPUs, the RTX 5080 and the RTX 5090 (about $1,500 and $3,000 GPUs, respectively). The 5080 clocks in at 1,800 TOPS, while the 5090 pushes a powerful 3,400 TOPS. Those also come alongside power draws of 360 and 575 watts, respectively.
For a dedicated automotive AI chip to be able to place itself squarely in the middle of those performance numbers is quite a feat, especially given Tesla’s previous hardware. HW3 clocked in at a measly 144 TOPS, while HW4/AI4, the current generation, pulls in at around 500 TOPS, a solid 3- 5x leap over HW3.
During a past earnings call, Elon claimed AI5 could be as much as 10 times more capable than HW4, which would imply an astronomical 5,000 TOPS. The 2,000 to 2,500 TOPS figure from this new report, however, represents a 4- to 5-fold generational jump, which feels more grounded and aligned with recent performance improvements elsewhere.
What is a “TOPS”?
TOPS is essentially a raw measure of processing power for a specific type of math, one related to the math used by neural networks. For an AI like FSD, it's the single most important metric. Think of it like the AI’s IQ - more TOPS means the computer can think faster and process more information, letting it better understand the environment around the vehicle and make smarter decisions.
True Performance or Skewed?
The key to understanding Elon’s claims about the TOPS figures lies in specialization. Tesla’s FSD computer is what is known as an ASIC - an Application-Specific Integrated Circuit. Unlike a general-purpose GPU in a gaming PC, Tesla’s AI hardware is designed from the ground up for one singular purpose: running the specific types of neural networks that FSD relies on.
This focus allows for incredible efficiency and performance in its designated tasks, and Tesla likely measures performance internally against AI inference benchmarks built around FSD.
The rumor that Tesla is tapping both Samsung and TSMC is pretty significant here as well. Tesla has previously sourced its chips from Samsung but likely requires additional capacity from TSMC, the world’s largest chip fabricator. A multi-source structure like this means Tesla is already putting the pieces together to mitigate supply chain risks.
AI Powerhouse
The need for AI5’s immense power isn’t just about running the current version of FSD, but about being able to support future versions of FSD that may require more computing power. Tesla continues to increase the size of thei AI models, which means that they’ll require more memory. One of the challenges in autonomy is that decisions must be made in just fractions of a second so that the vehicle can react accordingly. If output wasn’t required in nearly real-time, the vehicle could analyze video frames for a longer period and come up with better output, but the need for output in a timely fashion makes computing power critical.
Tesla’s executive team has repeatedly mentioned that the path towards fully Unsupervised FSD and Robotaxi lies in massive computational power alongside redundancy. The system will need to run increasingly complex neural networks to handle edge cases with greater reliability and start the march of 9s (improving from 99% to 99.9% to 99.99%, and so on). Layers of redundancy and multiple checks during the decision-making process will also be required for safety, which also requires additional compute.
What About HW3 and AI4?
With all this talk of AI5, the immediate question for every current Tesla owner and short-term buyer is: “What about MY car?”
AI4 is currently Tesla’s gold standard, and what they’re building today’s FSD, including FSD Unsupervised, around. For now, it offers Tesla enough headroom to continue expanding the neural nets and pushing new builds, but eventually, it too will one day need an upgrade.
Tesla has already stated that AI5 and AI6 will progressively improve FSD and become safer, but that doesn’t mean previous vehicles will be upgraded. Vehicles will only be upgraded if they’re not able to run Unsupervised FSD at a rate that’s safer than humans. Newer models will always perform better and at higher safety levels, but that doesn’t mean older hardware won’t be capable of safe driving.
The real story here is HW3. While Tesla’s executives have previously said that Hardware 3 is “Robotaxi Ready,” the practical reality of FSD V12.6 and V13.2 has set in for many. With FSD V13 pushing the envelope today, and Tesla’s intent to upgrade HW3 vehicles if they can’t figure out a solution, it seems the end of the line is coming.
For owners of HW3 vehicles, this likely means Tesla is planning a retrofit based around AI5 - likely a lower-performance version that will fit the current HW3 power and cooling packages. There could be a similar solution in the future for AI4 vehicles if Tesla plans to address the other half of the fleet, but that’s likely years away and only if they’re not able to achieve autonomy on that hardware.
The neural nets required for FSD to drive itself without supervision in complex urban environments will be orders of magnitude more complex than what we see today in just a few years. AI5 isn't just an upgrade; it's the necessary hardware to advance FSD to the next level.
In the race to deploy autonomous vehicles, there have been two schools of thought. One is led by sensor fusion, which means the more sensors and the more types, the better. The other is Tesla’s school of thought — vision.
So far, even Google’s CEO, Sundar Pichai, has described Tesla as the leader in the autonomy sector.
Google CEO on who is the leader in self-driving space: "I think obviously @Tesla is a leader in the space. It looks to me like Tesla and Waymo are the top two." pic.twitter.com/T0hlSICm8V
A new analysis from Bloomberg (paywall) offers a similar perspective, focusing on the numbers and real-world safety metrics. Tesla’s strategy isn’t just viable - it is far outpacing its competitors.
A Tale of Crash Rates
The most striking numbers from Bloomberg’s analysis are safety-related. According to their comparison, FSD reports approximately 0.15 crashes per million miles driven. In contrast, Waymo reported approximately 1.16 crashes per million miles.
That means that a Tesla using FSD is seven times less likely to be involved in a crash than a Waymo vehicle, even with its bevy of sensors. This is in line with Tesla’s latest vehicle safety report, which notes that a Tesla using FSD is 10 times less likely to be involved in an accident than a driver in any other vehicle.
Crash rates compared
Bloomberg
When it comes down to it, sensor fusion, while it can be fantastic, it simply provides too much data to process and analyze. While LiDAR, radar and cameras all have their unique advantages, cameras end up being the most versatile. Our roads and world were created around vision and audio, so a LiDAR-only vehicle can’t navigate our roadways since it would be unable to see signs or any other object that lacks depth. For LiDAR to be useful, it needs to be coupled with vision.
Vision works well because it applies to all situations, and it’s a system that continues to improve thanks to advancements in image processing and AI. While measurements with vision still lag behind LiDAR, they’ve reached a point where they’d “good enough,” and the millimeter-level accuracy of LiDAR isn’t needed.
When radar and vision disagree, which one do you believe? Vision has much more precision, so better to double down on vision than do sensor fusion.
Besides the difficulty of using sensor fusion, Bloomberg also points out that Tesla’s advantage is in the fundamental cost of the hardware. The Model Y costs just 1/7th of the total cost of a Waymo vehicle.
This enormous cost difference is a direct result of how Tesla and Waymo are approaching autonomy. Waymo’s vehicles are high-end, third-party electric cars, like the now-discontinued Jaguar I-Pace, which are then retrofitted with an expensive, custom-built suite of sensors. This sensor suite includes multiple LIDAR units, radars, and cameras.
Tesla’s ability to scale autonomous driving faster than its rivals gives it an edge in the self-driving race, says Bloomberg Intelligence's Steve Man https://t.co/B1x5Jhx6Lfpic.twitter.com/XYPCblWmXn
Tesla, meanwhile, includes all the hardware for autonomy as standard equipment on each of their vehicles, with a relatively inexpensive suite of cameras and its own in-house designed FSD computer. Using affordable hardware means it’s easy to produce and field more vehicles, resulting in more data.
On top of that, building more vehicles at a lower price creates a larger and larger economic difference as time goes on, as Tesla’s Robotaxis become profitable far quicker than Waymo’s.
3 Billion Miles… and Counting
The biggest advantage that Tesla has over any other entrant into the autonomy ring is simply just data. Tesla’s fleet has gathered over 3 billion miles of driving data globally, whereas Waymo’s fleet is just a minuscule 22 million miles.
Putting that into perspective, for every mile driven by a Waymo vehicle, a Tesla has driven over 135. Tesla’s advantage is also the fact that its data is global. It includes vehicles operating in a range of environments, from deserts to the Arctic, from cities to extremely rural areas, and is capable of achieving generalized autonomy.
Waymo’s data is extremely focused on urban and suburban areas and is effectively unusable for generalized vehicle autonomy. A larger, more capable fleet is the key to providing an effective robotaxi service, after all.
Scaling Manufacturing
Finally, Waymo doesn't produce vehicles. Tesla produces Robotaxis from scratch - every vehicle off the line has the ability to run Unsupervised FSD, and eventually join the Robotaxi fleet. Waymo needs to partner with other companies that have a good platform, and they must adapt their technology to that platform.
Waymo’s fleet is expected to be 2,500 vehicles by the end of 2025, while Bloomberg expects Tesla’s functional fleet to hit 35,000 by the same time. That’s not even counting the millions of AI4-powered vehicles that could also join the fleet by late 2026.
Overall, Tesla is a clear winner in the Robotaxi race - and it isn’t just because of one element. They’re winning through data, cost, and scalability, and the gap will only continue to grow.
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