Today, the machine learning algorithms are extensively used to find the solutions to various challenges arising in manufacturing self-driving cars. With the incorporation of sensor data processing in an ECU (Electronic Control Unit) in a car, it is essential to enhance the utilization of machine learning to accomplish new tasks. The potential applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors – like lidar, radars, cameras or the IoT (Internet of Things).
The applications that run the infotainment system of a car can receive the information from sensor data fusion systems and for example, have the capability to direct the car to a hospital if it notices that something is not right with the driver. This application based on machine learning also includes the driver’s speech and gesture recognition and language translation. The algorithms are classified as an unsupervised and supervised algorithms. The difference between both of them is how they learn. An autonomous car is a vehicle capable of sensing its environment and operating without human involvement. A human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present in the vehicle at all. An autonomous car can go anywhere a traditional car goes and do everything that an experienced human driver does. Engineers have been attempting prototypes of self-driving cars for decades. The idea behind it is really simple: Outfit a car with cameras that can track all the objects around it and have the car react if it’s about to steer into one. Teach in-car computers the rules of the road and set them loose to navigate to their own destination.
This simple description elides a whole lot of complexity. Driving is one of the more complicated activities humans routinely do. Following a list of rules of the road isn’t enough to drive as well as a human does, because we do things like make eye contact with others to confirm who has the right of way, react to weather conditions, and otherwise make judgment calls that are difficult to encode in hard-and-fast rules.
John Krafcik, CEO of Waymo, presents a self-driving car at Wed Summit in Lisbon, Portugal, on November 7, 2017. Horacio Villalobos/Corbis/Getty Images
And even the simple parts of driving — like tracking the objects around a car on the road — are actually much trickier than they sound. Take Google’s sister company Waymo, the industry leader in self-driving cars. Waymo’s cars, which are fairly typical of other self-driving cars, use high-resolution cameras and lidar (light detection and ranging), a way of estimating distances to objects by bouncing light and sound off things.
The car’s computers combine all of this to build a picture of where other cars, cyclists, pedestrians, and obstacles are and where they’re moving. For this part, lots of training data is needed — that is, the car has to draw on millions of miles of driving data that Waymo has collected to form expectations about how other objects might move. It’s hard to get enough training data on the road, so the cars also train based on simulation data — but engineers have to be sure that their AI systems will generalize correctly from the simulation data to the real world.
That’s far from a complete description of the systems at work when a self-driving car is on the road. But it illustrates an important principle to keep in mind when wondering where our self-driving cars are: Even the “easy” things turn out to hide surprising complexity.