#Chitchat nails how to
#Chitchat nails drivers
#Chitchat nails driver

There is plenty of equity cushion and bond markets are rallying a bit now, paradoxically supporting the investment securities on the balance sheet of SVB.

May be 10 years ago it would have been a good fit, but not now.Įither way, not much for SVB depositors to worry other than the fact that their immediate liquidity needs may take some time to be met. SVB simply is not a bank to serve large corporations like Tesla.

#Chitchat nails drivers

This is where large training sets of how this judgement call was made by various drivers in similar situations is used to train the neural network to make similar judgement calls, much as the human being a passenger in the car for many years prior to learning to drive taught them by their parents' examples.Ĭlick to expand.Can't say for sure, but even if Tesla has some cash at SVB, it is unlikely to be significant and even if it was, they will eventually recover it all.

#Chitchat nails driver

Is the object even real, or a phantom object based on infinite combinations of lighting / roadway conditions / etc? Is it a small cardboard box or a small piece of wood or a piece of wood with nails sticking up from it? Better to run over it and risk object impact or brake hard and risk being hit from behind or swerve and risk a side collision? Without #1 above, which the human driver already has but the computer driver does not yet have, making judgement calls other than 'brake hard to avoid collision with any object' (the LIDAR-based approaches) is very challenging. Even something as simple as 'avoid the object ahead of you in the current lane' is often an easy judgement for the human only because of #1, but incredibly challenging otherwise.

Human judgement of competing priorities is both A) challenging to replicate in such an approach as the number of one-off edge cases is (for all intents and purposes) infinite, and B) frankly, quite variable from human to human. This is an extremely hard problem that the human brain has already solved (and is surprisingly good at, in fact) but which the car needs to solve to be on the same footing as the human driver at the start of the 'learning to drive' phase.Ģ. Tesla's approach is to try to give the car the same understanding of the objects in the world around that a human driver has, based on the same optical-field input that a human has (albeit in 360-degrees). LIDAR-based solutions are attempting to minimize the need for understanding the objects in the world around them in favor of a focus on object avoidance this results in many limitations. Without an understanding of the objects in the world around the car, self-driving likely cannot happen. Procedural programming a car to follow the rules of the road could work fairly well for no-traffic (vehicular / bicycle / pedestrian), very well maintained roadways without any foreign objects.

#Chitchat nails how to

Human drivers have an understanding of the objects in the world around them and how to interact with them that vehicles do not have. There are two key areas which make the Dynamic Driving Task (DDT) a far harder problem to solve than it appears, and hence requires a far different and much harder approach:ġ. This is a reasonable 'first-attempt' approach, and was (generally speaking) the approach to implementing self-driving that was used in developing Tesla's highway stack and other 'first-attempts'. Humans are amazingly good at object recognition, hence it seems easy to us. TL DR: FSD is mostly an object recognition challenge. Object recognition in fog, and rain, with light bouncing off nearby surfaces, water droplet distortion on the lens, mud on another lens. Not just cost of cameras + wiring, but the extra CPU processing of more data.Ģ) is the meat-and-potatoes. The difficulty of 2) will depend slightly on 1), but adding cameras gets complex, and expensive very fast. You probably use a ton of fuzzy logic, and weights, and frankly using a neural net is likely very helpful here too, but its really not that hard, given the insane clockspeed of CPUs, and the relative slowness of the surrounding world.ġ) is a hardware issue. This is stuff you can do in plain old-fashioned C++. Deciding how to navigate, given this 4d representationĪs a programmer, I reckon 3) is WAY WAY easier than the other 2.Parsing that data into a 4d representation of your surroundings.I think its worth breaking the FSD challenge into 3 bits: