Stuff that caught my eye:
- Even state-less SOD such as stop signs can be complex
- active states and modifiers
- active states and modifiers
- temporal flickering in shadow mode indicates corner case
- Test driven feature development
- BEVNet to learn local map from camera images
- Pseudo-lidar (Vidar) approach is promising in urban driving (40mx40m range)
- infrastructure: operational vacation
- Other pics
- The grand mission: Tesla is ditching radars. They are using neural network and vision to do radar depth + velocity sensing.
- In order to do that, they need a large AND diverse 4D (3D+time) dataset. This is also used to train FSD.
- Tesla has a whole team spending about 4 months focusing on autolabeling
- Tesla uses MANY (221 as of mid-2021) triggers to collect the diverse dataset. They ended up with 1 million 10-second clips.
- Dedicated HPC team. Now Tesla training with 720 8-GPU nodes!
- Tesla argues that vision alone is perfectly capable of depth sensing. It is hard and it requires the fleet.
PMM: pedal misuse mitigation
Tesla's data set-up.
Have to figure out the road layout the first time the car goes there (drive on perception). Fundamental problem: Depth estimation of monocular
Once in a while radar gives you a FP that is hard to handle
Validation process
