My perspective is that solving Full Self-Driving (L4 autonomy) is essentially a two stage process:
Stage 1: "Feature Complete"
This is the initial stage where you put the right hardware and software in the car and it is able to do all the basic functions that a self-driving car needs to be able to do (stay in the lane, move with traffic, make lane changes, stop at stop signs and red lights, navigate intersections, follow nav directions, etc). During this stage, the system is not reliable enough to be L4 so driver supervision is required (safety driver) but the goal at this stage is just getting the pieces in places.
This stage is now relatively straightforward I think. Most self-driving companies, like Waymo, achieved this stage awhile ago. Even newcomers seem to achieve this stage relatively quickly since the hardware and software is common. Get a powerful Nvidia or eyeQ4 computer, LIDAR, radar, some high def cameras and the right software and you are pretty much good to go. LIDAR definitely helps you achieve this stage quicker too since LIDAR can provide the car with high accuracy mapping of the car's surroundings.
Tesla is just now finishing up this stage because they did things the hard way. They rejected LIDAR (for various reasons I won't rehash here because it is off topic) and went with camera + radar + ultrasonic hardware that puts much more emphasis on camera vision. Basically, camera vision has to do what the LIDAR would normally do. So the camera vision has to be much more sophisticated. But based on the Autonomy Investor Day event, it appears that Tesla has perfected their camera vision where it is able to detect and track objects with high accuracy.
Stage 2: "The March of 9's"
This is the perfecting stage. Once you have the pieces together, you have to make the system better and better until it gets so good and so reliable that the driver can safely not pay attention anymore. Of course, 99% might sound very good but it is not good enough considering how many miles a car drives in the US every day. You really have to get to something like 99.9999% before your car is good enough to be L4 autonomous. Hence the term "march of 9's". You perfect your self-driving, adding another 9 each time until eventually you have enough 9's that your system is L4 autonomous.
This stage is the tricky stage and it is where the leaders in self-driving are currently working on. There are probably millions and millions of edge cases and driving cases that a car needs to learn how to handle in order to be L4 autonomous. Plus, human drivers can be very unpredictable. So it is difficult to teach an autonomous car to handle all the crazy situations it might face.
This is where I think Tesla's fleet learning will pay off big time. These edge cases happen all over the world, on different roads, in different weather conditions etc... There is no easy way to solve all the edge cases and driving cases with simulations. They are just not going to cover everything. And a small fleet of cars or a fleet that is very localized will also miss a ton of driving situations. The best way, statistically, to solve all the millions of driving cases, is to have a huge number of cars spread out over the entire US for example, because those cars will experience a much greater variety of driving cases every day. So this is why I think that Tesla's fleet learning is a big deal. With hundreds of thousands of cars on roads all over the world, Tesla will get to all those edge cases and driving cases much faster. When Tesla gets to 1 million cars on the road in a couple years, that data will be even bigger.
This is why I am optimistic that Tesla will achieve general full self-driving at some point. Solving the edge cases requires massive data and Tesla has that. Now, other companies can get to local full self-driving by focusing on just one geographical area and training their cars to handle that specific are. This is what Waymo is doing and I think they will be successful. But Tesla has an advantage to getting to full self-driving in a more general way because of the data that Tesla has. As the numbers of cars grow, so will the data which will accelerate the growth of the machine learning. It is exponential. And really exponential growth is the only way to solve a big data problem in a timely manner. It's like if I tell you to solve 100 billion puzzles. Trying to solve that many puzzles by hand would take forever. Just getting even a large number of people, say 100,000, to solve them, would still take way too long. But if I grow the number of people solving the puzzles exponentially, I will solve the puzzles in a reasonable amount of time.
Stage 1: "Feature Complete"
This is the initial stage where you put the right hardware and software in the car and it is able to do all the basic functions that a self-driving car needs to be able to do (stay in the lane, move with traffic, make lane changes, stop at stop signs and red lights, navigate intersections, follow nav directions, etc). During this stage, the system is not reliable enough to be L4 so driver supervision is required (safety driver) but the goal at this stage is just getting the pieces in places.
This stage is now relatively straightforward I think. Most self-driving companies, like Waymo, achieved this stage awhile ago. Even newcomers seem to achieve this stage relatively quickly since the hardware and software is common. Get a powerful Nvidia or eyeQ4 computer, LIDAR, radar, some high def cameras and the right software and you are pretty much good to go. LIDAR definitely helps you achieve this stage quicker too since LIDAR can provide the car with high accuracy mapping of the car's surroundings.
Tesla is just now finishing up this stage because they did things the hard way. They rejected LIDAR (for various reasons I won't rehash here because it is off topic) and went with camera + radar + ultrasonic hardware that puts much more emphasis on camera vision. Basically, camera vision has to do what the LIDAR would normally do. So the camera vision has to be much more sophisticated. But based on the Autonomy Investor Day event, it appears that Tesla has perfected their camera vision where it is able to detect and track objects with high accuracy.
Stage 2: "The March of 9's"
This is the perfecting stage. Once you have the pieces together, you have to make the system better and better until it gets so good and so reliable that the driver can safely not pay attention anymore. Of course, 99% might sound very good but it is not good enough considering how many miles a car drives in the US every day. You really have to get to something like 99.9999% before your car is good enough to be L4 autonomous. Hence the term "march of 9's". You perfect your self-driving, adding another 9 each time until eventually you have enough 9's that your system is L4 autonomous.
This stage is the tricky stage and it is where the leaders in self-driving are currently working on. There are probably millions and millions of edge cases and driving cases that a car needs to learn how to handle in order to be L4 autonomous. Plus, human drivers can be very unpredictable. So it is difficult to teach an autonomous car to handle all the crazy situations it might face.
This is where I think Tesla's fleet learning will pay off big time. These edge cases happen all over the world, on different roads, in different weather conditions etc... There is no easy way to solve all the edge cases and driving cases with simulations. They are just not going to cover everything. And a small fleet of cars or a fleet that is very localized will also miss a ton of driving situations. The best way, statistically, to solve all the millions of driving cases, is to have a huge number of cars spread out over the entire US for example, because those cars will experience a much greater variety of driving cases every day. So this is why I think that Tesla's fleet learning is a big deal. With hundreds of thousands of cars on roads all over the world, Tesla will get to all those edge cases and driving cases much faster. When Tesla gets to 1 million cars on the road in a couple years, that data will be even bigger.
This is why I am optimistic that Tesla will achieve general full self-driving at some point. Solving the edge cases requires massive data and Tesla has that. Now, other companies can get to local full self-driving by focusing on just one geographical area and training their cars to handle that specific are. This is what Waymo is doing and I think they will be successful. But Tesla has an advantage to getting to full self-driving in a more general way because of the data that Tesla has. As the numbers of cars grow, so will the data which will accelerate the growth of the machine learning. It is exponential. And really exponential growth is the only way to solve a big data problem in a timely manner. It's like if I tell you to solve 100 billion puzzles. Trying to solve that many puzzles by hand would take forever. Just getting even a large number of people, say 100,000, to solve them, would still take way too long. But if I grow the number of people solving the puzzles exponentially, I will solve the puzzles in a reasonable amount of time.
