Tesla's FSD and AEB prevent a possibly deadly accident
Guri C
In a harrowing incident posted on X.com, Tesla owner Guri C publicly thanked Elon Musk and Tesla's Full Self-Driving system and the Automated Emergency Braking (AEB) feature for averting what could have been a tragic accident.
According to Guri C, FSD was engaged, but the Model Y would not move forward when the light turned green. Despiting pushing the accelerator to take control, the Model Y refused to move forward, suddenly hitting the brakes instead. Seven seconds after the light turned green, a vehicle ran the red at a high rate of speed, and it would've been a terrible and possibly deadly collision if the Tesla had not intervened.
FSD Paid for Itself
Guri C's firsthand testimony of the incident on X.com initiated a lot of reactions, emphasizing the safety net that autonomous technology provided in unforeseen circumstances. Several individuals applauded the advanced technology in Tesla vehicles, pointing out that the reluctance of the FSD system to accelerate even under human command possibly saved lives in this instance.
Some highlighted the remarkable integration of the continuously evolving neural network powered by multiple cameras and AI chips, which works tirelessly behind the scenes, ensuring the vehicle's autonomous responses are always a step ahead in recognizing potential dangers on the road.
Recent Tesla Enhancements
In a deep dive into Tesla's safety features prompted by the incident, the term "AI Guardian Angel" emerged, symbolizing the protective instinct of the vehicle empowered by artificial intelligence. Discussions spotlighted Obstacle-Aware Acceleration, another safety feature that corroborates with the FSD to take necessary actions when confronting unforeseen hazards, indicating the multilayered safety net that Tesla has woven into its autonomous driving features.
The narrative portrayed by Guri C stirred debates distinguishing between FSD and AEB, with the latter being the system that eventually halted the car, clearly demonstrating the life-saving feature engineered into Tesla vehicles. The AEB feature got a significant upgrade in April, which may have been the difference between life and death in this situation.
This life-saving event showcases the advancement in autonomous technology, illustrating a near future where AI operates with heightened awareness, actively preventing accidents and ensuring road safety. This incident, echoing Tesla's data affirming the safety of autonomous vehicles, builds confidence in the shift towards autonomous driving.
Tesla's commitment to safety and innovation shines in this instance, reinforcing belief in a future where roads are safer with autonomous technology, a vision spearheaded by Elon Musk, making a safer autonomous future not just a possibility but an impending reality.
Tesla has unveiled its 6-seat Model Y variant in China, known as the Model Y L. This new variant of one of the world’s best-selling vehicles comes with a longer wheelbase, adjusted C-pillar design, and most importantly, a six-seat interior layout.
The vehicle’s specifications have been officially listed in a filing with China’s Ministry of Industry and Information Technology (MIIT), confirming a launch for this fall.
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The addition of a longer wheelbase and a more spacious third row is a fantastic addition for the Model Y’s family utility, and positions this variant as sort of a mini Model X, but let’s compare the sizes to really know how this new Model Y compares to a Model X.
Meet the Model Y L
The defining feature of the new Model Y L is its six-seat configuration. This layout has previously been exclusive to the larger and more expensive Model X. While Tesla has offered the Model Y in a 7-seat configuration before, the third row was much too small to be utilized by anyone but small children.
Comparing Model Y L to the Model X
@xiaoteshushu on X
Let’s compare this upcoming Model Y L to the regular Model Y and the Model X.
Vehicle/Dimension
Wheelbase
Overall Length
Model Y
2,890mm / 113.8 in
4,797mm / 188.9 in
Model Y L
3,040mm / 119.7 in
4,976mm / 195.9 in
Model X
2,965mm / 116.7 in
5,060mm / 199.2 in
The new wheelbase of 3,040mm is a significant stretch from the standard wheelbase, and in fact, is longer than the Model X’s wheelbase of 2,965mm. However, the overall length of the vehicle is 84mm (~3 inches) shorter than the Model X. This means the vehicle sits neatly between the current Model Y and Model X, filling a much-needed gap.
While this Model Y L is slightly smaller than the Model X, it doesn’t necessarily mean that it’s smaller inside. The Model X features a much larger front end than the Model Y, accounting for several inches. When you line up the front wheel base of the Model X with this new Model Y, the vehicles are almost exactly the same length.
Tesla has designed this Model Y to be a bit more compact and efficient than the Model X, and likely much cheaper, while featuring the well-loved design of the new Model Y.
Other Specifications and Price
The MIIT filing also provided a detailed look at some additional specifications. The Model Y L is a dual-motor, AWD variant, so it will likely be more expensive than the current Model Y AWD that’s available in China today. Tesla charges an additional $6,500 USD when upgrading the Model X from a 5-seat configuration to a 6-seat layout, so we may see something similar here.
The extra length has been added behind the C-pillar, resulting in a longer rear profile for the Model Y L. To accompany this, Tesla has added an updated rear spoiler, similar to the one found on Performance variants, but not carbon fiber. There is also a new wheel design to complement the updated look, along with unique Model Y L badging and a new light gold paint option.
In classic Tesla fashion, no Tesla is slow - and the Y L has a 0-100 km/h (0-60mph) time of 5.9s, with a top speed of 217km/h. Alongside an 82.5 kWh LFP battery pack, the Model Y L boasts an impressive CLTC range of 688 km (427 mi).
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Launch & Availability
According to posts from Tesla China on Chinese social media, the new Model Y L is scheduled to launch in the fall of 2025. Its official listing in the MIIT database is essentially the final regulatory step required before sales can begin, which means the launch is really just around the corner. For now, it appears that Tesla intends to launch this vehicle only in China, as no other filings have been made in other regions. However, these could be revealed in the coming months.
The new Model Y L is a huge addition to Tesla’s lineup - one that addresses the Chinese preference for vehicles with longer wheelbases and additional passenger room in a compact SUV package. The question is - will this variant make its way to North America and Europe?
Solving real-world artificial intelligence - whether for autonomous driving, real-world robotics, or advanced reasoning - requires an almost unfathomable amount of computational power. To meet this challenge, Tesla has been developing its own custom AI training hardware while simultaneously purchasing hardware in the open market.
Now, the next-generation Dojo 2 chip has reportedly entered mass production with the world’s largest semiconductor manufacturer, TSMC. While many may consider this a side quest, expanding Tesla’s computing base will be necessary to achieve exascale supercomputing, which will be crucial for all of Tesla’s AI ambitions.
Elon Musk called Dojo 2 “a good computer,” and then followed up with a classic computer performance joke - Dojo 2 can indeed play Crysis at a billion frames per second.
While Tesla has effectively utilized powerful third-party GPUs to train its models to date, the Dojo supercomputer is a ground-up, application-specific solution designed for a single purpose. It will efficiently process massive amounts of video data for training neural networks. The Dojo 2 chip itself is the key that unlocks this potential.
Dojo 2 will train the vision-based neural nets that FSD relies on, allowing Tesla to process video from its massive global fleet of vehicles even faster. As Tesla continues to improve FSD, one of the biggest challenges has been the intake of video for handling difficult edge cases.
Hundreds of thousands of miles of training data may pass by before an edge case is identified and trained on, but it all needs to be analyzed, labeled, and processed, which is key for Dojo 2. Each new useful piece of training data will help Tesla proceed down the march of 9s, making FSD just that little bit better every time.
This process requires massive amounts of compute and training time - but it is an absolute necessity to improve FSD. Of course, this goes beyond just FSD in vehicles. Tesla’s humanoid robot, Optimus, also runs on FSD to navigate and interact with the physical world.
While it may be a custom version of FSD, it remains FSD at its core, which means the same neural nets that analyze the environment and build a 3D map of the world for your car perform the same work for Optimus.
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Custom Approach to AI Hardware
Dojo 2’s power doesn’t just come from raw compute; it comes from a series of architectural choices that make it excel at training FSD and differentiate it from general-purpose hardware, or even other AI-specific hardware.
To this end, Tesla is using TSMC’s new Integrated Fan-Out with Silicon-on-Wafer (InFO-SoW) packaging technology. For massive AI workloads, heat and the speed at which data moves between chips are often the biggest bottlenecks.
This new packaging technique allows for high-bandwidth connections directly between processing dies, which lowers latency and dramatically improves heat dissipation, all key to building massive and dense compute clusters.
Unlike general-purpose chips, Dojo 2 is designed with a custom instruction set, specifically built to train FSD. The cores are specifically made to accelerate the exact mathematical operations, like matrix multiples and systolic arrays, which form the backbone of Tesla’s vision-based neural networks.
By building its own hardware, Tesla can then integrate its own software and compilers directly with the silicon, optimizing for specific workloads and avoiding the performance penalties that can result from using third-party software, such as Nvidia’s CUDA.
The start of Dojo 2 may seem like a side quest for some, but it’s actually a key step for Tesla’s AI technologies that give them an advantage over the competition using off-the-shelf hardware. They’ll need to continue investing in custom hardware to improve FSD at a reasonable pace, rather than the current glacial pace we’ve seen over the last few months.
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