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Tesla AI Day
Deep Understanding Tesla FSD Part 1: HydraNet
From Theory to Reality, Analyze the Evolution of Tesla Full Self-Driving
Almost a month ago Tesla hosted Tesla AI Day. In this event, Tesla introduced AI & autopilot completely and in detail for the first time.
As an AI practitioner, especially if you focus on the autonomous driving domain, you should study the first part of Tesla AI Day. A few weeks after the event, I reviewed the video “frame by frame”, searched, downloaded, read all the papers involved in the video, and took a lot of notes. Gradually, I outlined the architecture of Tesla’s FSD.
Next, I will try to explore how Tesla fulfilled its promise of artificial intelligence & autopilot from the perspective of a software engineer.
Before starting, please think about a question with me. If you act as Sr. Director of Tesla AI and lead AI Team, how will you achieve autonomous driving?
Cameras, Lidars, Machine Learning, Neural Network, Maps, HD Maps, Papers, Labels, Training, Testing, DataSets, Planning, Security, Chips, CPUs, GPUs, Mass Data Traning, ethics of AI…, all these things suddenly flooded my brain. The conclusion is that this is a mission impossible for me.
