The big difference is that the responsibility in case of an accident is transferred from the driver to the car.
This increases complexity significantly, because the car needs to handle all possible edge cases.
Regulations still don't allow L3 systems...
Level 3๏ธโฃ - Eyes off
The car does the driving in specific situations (e.g. on the highway). The car may hand back control to the driver, but it needs to give couple of seconds warning, because he may be distracted.
Many companies are working on L3 systems and BMW, Mercedes and Honda already announced specific plans. However, there are still regulatory hurdles that need to be overcome.
The FSD Beta of Tesla are still a L2 system!
Level 4๏ธโฃ - Mind off
The car drives itself and doesn't rely on a human as a fallback - there may be no one behind the steering wheel.
However, the car can only drive in certain areas and conditions, which it supports.
These restrictions are called ODD. Example: Waymo's service in Phoenix, which is geofenced and the safety driver is present if the environmental conditions are challenging.
Another company that is allowed to drive without safety driver is Cruise.
Level 5๏ธโฃ - Steering Wheel Optional
The ultimate stage of self-driving - the car can handle all situations under all conditions on its own. At this stage, you don't really need a steering wheel anymore.
However, we are still quite far from this... ๐คทโโ๏ธ
Summary ๐
So now you know that almost all systems that are today on the market are sill L2. Yes, even Tesla...
The only comany that arguably has a true L4 system is Waymo, but it still operates in very limited situations under optimal conditions.
If you liked this thread and want to read more about self-driving cars and machine learning give me a follow! ๐
I have many more threads like this planned ๐
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What are Convolutional Neural Networks? ๐๏ธ โญ๏ธ โฐ๏ธ
CNNs are an important class of deep artificial neural networks that are particularly well suited for images.
If you want to learn the important concepts of CNNs and understand why they work so well, this thread is for you!
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What is a CNN? ๐ค
A CNN is a deep neural network that contains at least one convolutional layer. A typical CNN has a structure like this:
โช๏ธ Image as input
โช๏ธ Several convolutional layers
โช๏ธ Several interleaved pooling layers
โช๏ธ One/more fully connected layers
Example: AlexNet
A good example - AlexNet
Throughout the thread I will be giving examples based on AlexNet - this is the net architecture that arguably started the whole deep learning revolution in computer vision!