What is Tesla's focus in developing autonomous driving?

Tesla's focus is on fast, efficient, and safe driving.

How many vehicles have received Tesla's full self-driving beta software?

Tesla has shipped the full self-driving beta software to 400,000 vehicles in the US and Canada.

What technologies does Tesla use to predict occupancy in 3D space?

Tesla uses a combination of radar, ultrasonics, and cameras to create occupancy networks that can predict occupancy in 3D space without labeling or ontology design.

How does Tesla predict and tokenize lanes for driving tasks?

Tesla predicts and tokenizes lanes using state-of-the-art genetic modeling techniques, including autoregressive transformers, to create vector representations for ease of use.

What is Tesla's approach to reconstructing barriers and vehicles?

Tesla uses a hybrid approach that combines neural networks to accurately reconstruct barriers and vehicles, including auto-labeling traffic lights without human input.

Keynote by Ashok Elluswamy at CVPR'23 WAD

Name: Keynote by Ashok Elluswamy at CVPR'23 WAD
Uploaded: 2023-07-11 00:55:23
Description: Tesla is utilizing advanced neural networks and machine learning to develop a comprehensive understanding of the world for autonomous driving, with a focus on fast, efficient, and safe driving, and exciting developments expected in the next 12 to 18 months.

This article is a summary of a YouTube video "[CVPR'23 WAD] Keynote - Ashok Elluswamy, Tesla" by WAD at CVPR

TLDR Tesla is utilizing advanced neural networks and machine learning to develop a comprehensive understanding of the world for autonomous driving, with a focus on fast, efficient, and safe driving, and exciting developments expected in the next 12 to 18 months.

Key insights

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The success of Tesla's self-driving stack is attributed to its modern machine learning-based approach, where components of the self-driving stack are integrated into neural networks, distinguishing it from the traditional localization-based approach.
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Tesla's real-time prediction of voxel occupancy based on multiple camera streams allows for efficient and continuous monitoring of space around the car, eliminating the need for offline post-processing.
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The multi-trip reconstruction technology allows Tesla to gather data from different cars driving around the world, enabling them to reconstruct entire scenes and obtain road lines and lanes directly from the fleet.
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Tesla's hybrid approach combines neural networks and offline reconstructions to accurately label barriers, vehicles, and even traffic lights without human input.
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Tesla's system can automatically brake to avoid collisions with vehicles that cut it off, making driving safer.
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The recent advancements in generative models like Transformers and diffusion have enabled the network to generate video sequences of the future, predicting not just for one camera but for all eight cameras around the car, with consistent colors and motion of objects in 3D.
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The ability of the neural network simulator to imagine different futures based on different actions is super powerful and can represent things that are hard to describe in an explicit system.
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Tesla aims to build a general driving stack that is human-like, fast, efficient, and safe, requiring a lot of compute power and making them a world leader in compute platforms.
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The occupancy module in Tesla's end-to-end system is used for collision avoidance, even in complex scenarios like construction vehicles with moving arms, where traditional modeling methods may struggle to represent them accurately.

Q&A

What is Tesla's focus in developing autonomous driving?

— Tesla's focus is on fast, efficient, and safe driving.
How many vehicles have received Tesla's full self-driving beta software?

— Tesla has shipped the full self-driving beta software to 400,000 vehicles in the US and Canada.
What technologies does Tesla use to predict occupancy in 3D space?

— Tesla uses a combination of radar, ultrasonics, and cameras to create occupancy networks that can predict occupancy in 3D space without labeling or ontology design.
How does Tesla predict and tokenize lanes for driving tasks?

— Tesla predicts and tokenizes lanes using state-of-the-art genetic modeling techniques, including autoregressive transformers, to create vector representations for ease of use.
What is Tesla's approach to reconstructing barriers and vehicles?

— Tesla uses a hybrid approach that combines neural networks to accurately reconstruct barriers and vehicles, including auto-labeling traffic lights without human input.

Timestamped Summary

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00:00
Tesla has released full self-driving beta software to 400,000 vehicles in the US and Canada, utilizing a self-driving stack driven by eight cameras and machine learning, while also using radar, ultrasonics, and cameras to create occupancy networks for predicting voxel occupancy in 3D space without labeling or ontology design.
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06:34
Tesla has developed a sophisticated auto-labeling pipeline using multi-modal models and data from millions of video clips to guide autonomous vehicles' actions, including the extraction of lane and road lines from camera footage.
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10:01
Tesla is using neural networks to accurately reconstruct barriers and vehicles, including auto-labeling traffic lights, enabling comprehensive understanding of the world for autonomous driving, and being the first to ship emergency braking for crossing vehicles.
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13:25
A neural network has been developed that can predict future video sequences, including object motion and depth, without 3D priors or human input, allowing for accurate lane prediction and simulation of different future scenarios.
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17:43
Tesla is focused on developing a fast, efficient, and safe driving stack, aiming to be a leader in compute, and is working on foundational vision models that can understand roads, vehicles, and future robotics platforms, leading to exciting developments in the next 12 to 18 months, including the ability to track and segment moving objects.
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21:02
Future prediction tasks are becoming effective, enabling the development of simulators and learning representations; auto-regressive models are used to predict lanes accurately and avoid blurry results; the voxel size in the occupancy network output can be adjusted for different applications based on the trade-off between memory and compute.
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24:42
The speaker discusses the application of generalized methods in humanoid robotics tasks and emphasizes that it should not be any different from autonomous driving, as it is all pixels and shapes.
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27:19
The alignment issue with map information in Tesla's model is not critical as low-definition maps are used for rough guidance on which roads and lanes to take.