One of the most exciting products of the AI era is the self-driving car. Self-driving cars are also one of the most mysterious things you hear about in AI these days. In this video what I want to do is share with you a somewhat simplified description of a self-driving car so that you understand how you can piece together multiple AI components in order to build these amazing things. Let's get started. These are the key steps for deciding how to drive your self-driving car. The car will take as input various sensors such as pictures of what's in front of the car or to the sides or behind, as well as maybe radar or Lidar, meaning laser sensor readings. Given these inputs of a picture and maybe other things, it then has to detect other cars. So given that, hopefully, you'll figure out there's a car there, as well as where are the pedestrians, because we want to avoid other cars as well as avoid pedestrians. Both car detection and pedestrian detection can be done with machine learning using input/ output or A to B mappings that takes as input the picture and maybe radar and Lidar, sends the inputs and tells us where are the other cars and pedestrians. Finally, now that you know where are the other cars and where are the pedestrians, you can then feed this information into another specialized piece of software, it's called a motion planning piece of software that plans the motion or plans the path that you want your car to take, so that you can make progress to your destination while avoiding any collisions. Once you've planned out the motion for your car, you can then translate that into specific steering angle for your steering wheel and acceleration and brake commands, so how much to step on the gas pedal, and how much to brake in order to get your car to move at the desired angle as well as speed. Let's look at the three key steps of car detection, pedestrian detection, and motion planning in more detail. Car detection uses supervised learning. So, you've already seen how a learning algorithm can take as input pictures like these and output the detected cars. For most self-driving cars rather than using only a front-facing camera, so a camera that looks forward, also often uses cameras that look to the left, to the right as well as to the back so it can detect cars not just to the front but all around it. This is usually done using not just cameras but other sensors as well such as radar and Lidar. Next is pedestrian detection, and using a pretty similar type of sensors as well as techniques, self-driving cars can detect pedestrians. Finally, I briefly mentioned a motion planning step. So, what is that? Here's an example. Let's say you're driving your car and there's this light blue car in front of you. The motion planning software's job is to tell you what is the path, shown here in red, you should drive in order to follow the road and not have an accident. So the motion planning software's job is to output the path as well as the speed at which you should drive your car in order to follow the road, and the speed should be set so that you don't run into the other car, but you also drive at a reasonable speed on this road. Here's another example. If there's this gray car parked on the right side of the road, so you want to overtake this stopped car, then the motion planning software's job is to plot a path like that to let you veer a little bit to the left and safely overtake a stopped car. So far I've given a rather simplified description of self-driving as comprising mainly these three components. Let's look at a bit more detail of how an actual self-driving car might work. This is a picture you've seen so far. Input image, radar, or Lidar, sensor readings into car detection and pedestrian detection, and that is then fed to motion planning to help you select your path and speed. Now in a real self-driving car, you would usually use more than just cameras, radar, and Lidar. Most self-driving cars today will also use GPS to sense its position as well as accelerometers, sometimes called an IMU, this means accelerometers, and gyroscopes as well as a map because we know that cars are more likely to be found on a road, pedestrians are more likely to be found on sidewalks, although they are sometimes found on the road as well. All this is usually additional information that's fed into detect cars and pedestrians as well as other objects, we'll talk about in a second. Rather than just detecting cars and pedestrians, in order to drive safely, you also need to know where these cars and pedestrians are going in the future. So, another common component of self-driving cars is trajectory prediction, where there's another AI component that tells you, not just the cars and pedestrians you found, but also where they're likely to go in the next few seconds, so you can avoid them even as they're moving. To drive safely requires more than just navigating other cars and pedestrians. You also need to know where are the lanes so you might detect lane markings. If there's a traffic light you also need to figure out where's the traffic light, and is it showing a red, yellow, or green signal. Sometimes there are other obstacles, such as unexpected traffic cones or maybe there's a flock of geese walking in front of your car. That needs to be detected as well so that your car can avoid even other obstacles than cars and pedestrians. On a large self-driving car team, it would not be that unexpected to have a team or maybe a few people working on each of the boxes shown here in red, and it's by building all of these components and putting them together that you can build a self-driving car. As you can tell both from this rather complex example of an AI pipeline, as well as the early example of the four-step AI pipeline for the smart speaker, sometimes it takes a team to build all of these different components of a complex AI product. What I'd like to do in the next video is share with you what are the key roles in large AI teams. If you're either a one-person or small AI team now, that's okay, but I want you to have a vision of what building a large AI team, maybe in the distant future, might look like. Let's go on to the next video.