Tesla
20 HN mentions · mentioned in 11 months, 2021–2025
3:03:20 · 2.6M views · Aug 20, 2021
Tesla's 2021 AI Day presentation covering vision, planning and control, auto-labeling, simulation, the Dojo training hardware and the Tesla Bot announcement, followed by a Q&A.
Treated as a primary source on how Tesla builds its self-driving stack, cited by timestamp for transformers, synthetic data, Dojo and the humanoid bot.
20 comments from Hacker News
Waymo has superior performance based on their historical statistics. It makes sense, since their lidar sensors capture more of the environment, and directly in 3D. Their AI also seems better QA’ed. The Tesla AI Day[0] surprised me as it showed they only had a simple architecture for a very long time, simply feeding barely processed camera pixels to a DNN and hoping for the best with little more than supervised learning off human feeds. Their big claim to glory was that they rearchitectured it to produce a 2D map of the environment… which I thought they had years ago, and is still a far cry from the 3D modeling that is needed. After all, sure, we humans only take two video feeds as input… But we can appreciate from it the position, intent, and trajectory of a wealth of elements around us, with reasonable probability estimates for multiple possibilities, sometimes pertaining to things that are invisible, such as kids crossing from nowhere when near a school. Cruise also seems to have better tech; they had a barely-watched 2h30 description of their systems[1] which shows they do create a richer environment, evaluate the routing of many objects, and train their systems on a very realistic simulation, not just supervised training, which means it can learn from very low-probability events. They have a whole segment on including the probability that unseen cars may travel from perpendicular roads; Tesla’s creeping hit-or-miss are well-documented on Youtube. [0]: this video [1]: [LINK]
[LINK] This is a rather intuitive mathematical proof that neural nets can emulate all functions in many dimensions. Knowing integrals (Calc 102) would help. With this information it’s either that self-driving is eventually possible or human brains are not mathematically representable (i.e. something akin to a soul). Meanwhile, the data collection/storage is getting better (all Teslas are continually testing FSD and reporting failure cases, synthetic generation of “good and unique” perfectly labeled data[0] is only a year or two “old”). Sensors/cameras are improving (and have a lot more room to improve). The compute hardware is getting better and cheaper (analog computers[1], TSMC 2nm and beyond, Apple/Google/Teslas neural cores, etc). Additionally, given Waymo is already in production without humans in PHX it’s arguable that we already have “autonomous vehicles”. We now just need them to improve slightly and for Waymo or similar to test in the top 25 cities in the US. Which is mostly a matter of capital and time not technology limits. Maybe weather will be an issue for a few more years/decades - but even if Waymo only operated in Summer, I’d be happy to reduce my Lyft costs - and with enough data and maybe better sensors the neural nets will be able to handle weather too. Especially if the car just goes slow. [0] this video [1] [LINK] [2] [LINK]
I spent time working with Andrej and the rest of the FSD team back in 2020/2021, and we had plenty of conversations on how human visual processing maps onto our neural network architectures. Our approach—transformer-based attention blocks, multi-scale feature extraction, and temporal fusion—mirrors elements of the biological visual cortex (retina → LGN → V1 → V2 → V4 → IT) which break down raw inputs and integrate them over time. It’s amazing how closely this synthetic perceptual pipeline parallels the way our own brains interpret the world. The key insight we discovered was that explicitly enforcing brain-like topographic organization (as some academic work attempts - such as this one here) isn't necessary - what matters is having the right functional components that parallel biological visual processing. Our experience showed that the key elements of biological visual processing - like hierarchical feature extraction and temporal integration - emerge naturally when you build architectures that have to solve real visual tasks. The brain's organization serves its function, not the other way around. This was validated by the real-world performance of our synthetic visual cortex in the Tesla FSD stack. Link to the 2021 Tesla AI day talk: this video
They use the key-value lookup/routing mechanism from Transformers to predict pixel-wise labels in bird view (lane, car, obstacle, intersection etc.). The motivation here is that some of the predictions may temporarily be occluded, so for predicting these occluded areas it may be particularly helpful to attend to remote regions in the input images which requires long-range dependencies that highly depend on the input itself (e.g. on whether there is an occlusion), which is exactly where the key-value mechanism excels. Not sure they even process past camera frames at this point. They only mention that later in the pipline they have an LSTM-like NN incorporating past camera frames (Schmidhuber will be proud!!). Edit: A random observation which just occurred to me is that their predictions seem surprisingly temporally unstable. Observe, for example, the lane layout wildly changing while it drives makes a left-turn at the intersection ( this video ). You can use the comma and period keys to step through the video frame-by-frame.
> The "images" are part information. This. It is a mashup of both. I can clearly visualize many 3D objects in my mind, as well as zoom, pan, scale, rotate, recolor, animate, distort them, etc. But it is not purely visual, because I often "see" opposite side of a cube momentarily, which I should not be able to from this perspective. So, it is not a rendering engine. It is not physically, spatially and temporally stable. Stability is lost especially as your focus shifts (e.g. "seeing" behind objects as your attention slips there) The best way I know to describe this is by old and new Tesla FSD predictions which are sampled here: this video Improved version is how people talk about visualization, but the old unstable version is how (at least for me) it mostly is.