When the foundational R&D work is done, prediction will not be limited by hand labelling. Deep learning for prediction is inherently self-supervised since the "ground truth" is the future, which can simply be observed by a car's perception system. (Learn more here.)Īccording to some experts, research and development is needed to bring deep learning for prediction into a state of maturity. In the future, hand-labelled datasets can fine-tune neural networks trained on oceanic amounts of unlabelled video. This allows for scaling of deep learning unconstrained by human labour, which is required for hand-labelling large datasets of images and videos. That means predicting parts of a video or a video frame from other parts of the video or the frame. In my opinion, the biggest advance in perception will come from mastering self-supervised learning on video. Self-driving cars need to do three things: As a point of comparison, if $30 billion were added to Tesla's market cap, with 184 million shares outstanding, the share price would increase by about $160.
I believe that as Tesla demonstrates more progress on autonomy following the upcoming release of a new, rewritten version of Autopilot, the market will begin to price in more expected value for robotaxis and for sales of partially autonomous software features. In this article, I'll explain why if any company deserves a $30 billion valuation for robotaxis, it's Tesla. While I'm not an expert, I've done extensive research and self-education on self-driving cars.
Does this discrepancy reflect a deep understanding of the underlying technology? I don't believe so. Meanwhile, it is difficult to find an investor or analyst who attributes even a dollar of value to Tesla ( NASDAQ: TSLA) for robotaxis. Waymo ( GOOG, GOOGL) has been awarded frontrunner status and recently attained a $30 billion valuation from private market investors.
I believe the market is profoundly misjudging the competitive landscape in autonomous driving.
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