I'm not 100% sure if I'm summarizing correctly, but essentially the current method Comma uses is it (1) Collects tonnes of videos of "good" drivers + their acti

I'm not 100% sure if I'm summarizing correctly, but essentially the current method Comma uses is it (1) Collects tonnes of videos of "good" drivers + their actions (steering etc). (2) Using an autoregressive model, predict action_t based on scene_t. (3) The issue is because we rely on teacher forcing, the error accumulates, causing the model to go out of whack after 10 seconds. I'm just making stuff up, but I'm assuming this is because of teacher forcing itself? LLMs for eg model sentences by predicting the next word, but retains the past state via the KV cache. Unsure if these systems have a KV cache equivalency? I'm assuming LLMs are in a different regime due to the sheer amount of data as well. Also I guess a simulation based system could work - ie by going around teacher forcing, and instead doing generation every say 30 steps, and making the dataset itself larger by training on the errors themselves. Sadly this means one has to make the simulation system. But maybe Comma uses some encoder CNN + some LSTM (or attention) based past state thingo? tbh I'm just making stuff up - ignore me - brain dump.