{"id":7340,"date":"2023-08-29T13:30:06","date_gmt":"2023-08-29T21:30:06","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=7340"},"modified":"2025-04-24T17:14:30","modified_gmt":"2025-04-24T17:14:30","slug":"computer-vision-at-tesla","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/computer-vision-at-tesla\/","title":{"rendered":"Computer Vision at Tesla"},"content":{"rendered":"\n\n\n\n\n
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This article has been updated on https:\/\/www.thinkautonomous.ai\/blog\/?p=computer-vision-at-tesla<\/a><\/p>\n<\/div>\n<\/div>\n

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Tesla\u2019s Autopilot<\/strong> is certainly the most advanced in the world. If Comma.ai is the Andro\u00efd, Tesla is definitely the Apple of self-driving cars. Recently, Tesla has released \u201cTesla Vision<\/strong>\u201d, their new system equipped only with cameras\u2026 making it one of the only companies in the world not to use RADARs!<\/p>\n

This is not the only place where Tesla is going against the crowd, their entire strategy is based on fleet and selling products, while most of its competitors sell autonomous delivery and transportation services. You can learn more about it on my article on Tesla vs Waymo here.<\/a><\/p>\n

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In this article, I will explain you how the Tesla Autopilot works with just cameras<\/strong>, and we\u2019ll study their neural net in depth.<\/p>\n

You will learn a lot about how to implement a Computer Vision system in a self-driving car company like Tesla, but please note that I don\u2019t just publish about Tesla and Computer Vision on this blog, I also share daily content on my private emails on Self-driving cars and advanced AI technologies. If you\u2019d like to join, leave your email here and I\u2019ll see you tomorrow in your first email!<\/a><\/p>\n

Before we start, here\u2019s a short summary of what we\u2019ll learn:<\/p>\n

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  1. Introduction to Tesla Autopilot<\/strong><\/li>\n
  2. Tesla Vision \u2014 Driving with 8 cameras<\/strong><\/li>\n
  3. Hydranets \u2014 Tesla\u2019s Insane Neural Networks<\/strong><\/li>\n
  4. How Tesla Trains its neural networks (with Pytorch)<\/strong><\/li>\n
  5. Continuous Improvement & Fleet Leverage<\/strong><\/li>\n<\/ol>\n

    1. Introduction to Tesla Autopilot<\/h1>\n

    According to many self-driving car experts, Tesla is leading the self-driving car race! Its team of 300 \u201cJedi Engineers\u201d, as Elon Musk calls them, is solving some of the most complicated problems such as lane keeping<\/strong>, lane change, <\/strong>and cruise control. <\/strong>They also have additional tasks such as driving in a parking lot<\/strong>, Smart Summon<\/strong> <\/a>, and city driving<\/strong>.<\/p>\n

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    Tesla has to deal with all these tasks<\/figcaption>\n<\/figure>\n

    Tesla\u2019s tasks are well-known today. From lane detection, the most important feature of autonomous cars, to pedestrian tracking, they must cover everything and anticipate every scenario. For that, they use a Perception system made only with cameras called \u201cTesla Vision.\u201d<\/p>\n

    2. Tesla Vision \u2014 Driving with 8 cameras<\/h1>\n

    This is the view from the 8 cameras of a Tesla Model S.<\/p>\n

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    In 2021, Tesla switched to a vision only model; ditching the RADARs. I have written a complete article on the idea of transitioning to Vision only, and on the updates made on their neural networks here <\/a>.<\/p>\n

    In this article, we\u2019ll go back to the idea of Hydranets, Tesla\u2019s Neural Net, and we\u2019ll see how these models are trained in the company.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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    3. Hydranets \u2014 Tesla\u2019s Insane Neural Networks<\/h1>\n

    What is a HydraNet?<\/h1>\n

    Between the vehicles, the lane lines, the road curbs, the crosswalks, and all the other specific environmental variables, Tesla has a lot of work to do. In fact, they must run at least 50 neural networks simultaneously! <\/strong>That\u2019s just not possible on standard computers. To optimize for this, Tesla has created its own computer, and its own Neural Network architecture called a HydraNet.<\/p>\n

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    The neural networks are trained using PyTorch, a deep learning framework you might be familiar with.<\/p>\n

    Bird\u2019s Eye View<\/h1>\n

    Something else Tesla uses is Bird\u2019s Eye View: <\/strong>Bird\u2019s Eye View can help estimate distances and provide a much better and more real understanding of the environment. It can help with road curbes detection, smart summon, and other features.<\/p>\n

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    Smart summon in Tesla using Bird Eye View ( source <\/a>)<\/p>\n

    Some tasks run on multiple cameras. For example, Depth estimation is something we generally do on stereo cameras <\/strong>. Having 2 cameras helps estimate distances better. If you\u2019d like to learn more, I have an entire course on 3D Computer Vision and 3D Reconstruction <\/a>. In the meantime, here\u2019s their neural net for depth prediction.<\/p>\n

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    Depth estimation from 2 cameras<\/p>\n

    Tesla also has recurrent tasks such as road layout estimation<\/strong>. The idea is similar: multiple neural networks run separately, and another neural network is making the connection .<\/p>\n

    Road Layout Estimation<\/h1>\n
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    Optionally, this neural network can be recurrent so that it involves time. This type of architecture has been used a lot for road layout estimation using time sequence and continuity.<\/p>\n

    Something cool about the Hydranet is that it can be done to leverage only what\u2019s needed by the system: the lane line detection algorithm won\u2019t necessarily use the read or side cameras, etc\u2026<\/p>\n

    To process all of this, Tesla has built a computer they call the Full Self Driving (FSD) computer. I have written an article on Tesla\u2019s Hydranets in 2021, their computers, and on the switch from RADARs to Vision only, you can find it here <\/a>.<\/p>\n

    4. How Tesla Trains its Neural Networks (with Pytorch)<\/h1>\n

    How to train a Hydranet? <\/strong>According to Tesla\u2019s Team, training a hydranet with 48 heads on a GPU takes as low as 70,000 hours! That\u2019s almost 8 years. \ud83d\udc75\ud83c\udffd<\/p>\n

    In order to solve this, Tesla is changing the training mode from a \u201cround robin\u201d to a \u201cpool of workers\u201d.<\/p>\n

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    Here\u2019s the idea:<\/p>\n