[tmotwu]

I could not get through the entire article with straight face. It's riddled with fallacies and misconceptions on the problems currently facing self driving. It was especially this horrible take that put the credibility of this article into question:

> "A simple analogy can illustrate the conceptual difference between Computer Vision and LiDAR. Imagine two students, where one is just cramming and memorizing the content (LiDAR), while the other one is trying to really understand the material and truly learn it (Tesla FSD). The student that learned the material (Tesla FSD) will be able to answer the exam questions correctly, even if the questions on the exam are swapped, the questions are rephrased, or new components are added to the questions, while the student that memorized the content (LiDAR) will likely fail the exam."

Tesla's advantage isn't even it's computer vision systems - computer vision models don't exactly scale like language transformers (where larger, sparser parameters make better models). There hasn't been any significant advancements in vision models since Faster-RCNNs or YOLO, which is close to five years old now. Especially if you want to compare Tesla's SotA against Waymo, which has an army of Captcha labelers and a large plethora of example images.

The goldmine is in identifying and navigating around rare edge cases - data that can only be obtained with hundreds of thousands of hours of real world driving. It has very little to do with the correct set of camera and calibration configurations. The novel research is deep into safety verification strategies. Like how do can we predict human or object behavior by using miliseconds of prior movement of an object? Or how to use human arm movements and eye pupils to determine if a pedestrian or other driver is distracted? Can we avoid an accident if we have a better behavioral understanding of the scene?