Deep learning and computer vision approaches for SDCs

Perhaps the most exciting new technology in the world today is deep neural networks, especially convolutional neural networks. This is known collectively as deep learning. These networks are conquering some of AI's and pattern recognition's most common problems. Due to the rise in computational power, the milestones in AI have been achieved increasingly commonly over recent years, and have often exceeded human capabilities. Deep learning offers some exciting features such as its ability to learn complex mapping functions automatically and being able to scale up automatically. In many real-world applications, such as large-scale image classification and recognition tasks, such properties are essential. After a certain point, most machine learning algorithms reach plateaus, while deep neural network algorithms continually perform better with more and more data. The deep neural network is probably the only machine learning algorithm that can leverage the enormous amounts of training data from autonomous car sensors.

With the use of various sensor fusion algorithms, many autonomous car manufacturers are developing their own solutions, such as LIDAR by Google and Tesla's purpose-built computer; a chip specifically optimized for running a neural network.

Neural network systems have improved in terms of gauging image recognition problems over the past several years, and have exceeded human capabilities. 

SDCs can be used to process this sensory data and make informed decisions, such as the following:

• Lane detection: This is useful for driving correctly, as the car needs to know which side of the road it is on. Lane detection also makes it easy to follow a curved road.
• Road sign recognition: The system must recognize road signs and be able to act accordingly.
• Pedestrian detection: The system must detect pedestrians as it drives through a scene. Whether an object is a pedestrian or not, the system needs to know so that it can put more emphasis on not hitting pedestrians. It needs to drive more carefully around pedestrians than other objects that are less important, such as litter.
• Traffic light detection: The vehicle needs to detect and recognize traffic lights so that, just like human drivers, it can comply with road rules.
• Car detection: The presence of other cars in the environment must also be detected.
• Face recognition: There is a need for an SDC to identify and recognize the driver's face, other people inside the car, and perhaps even those who are outside it. If the vehicle is connected to a specific network, it can match those faces against a database to recognize car thieves.
• Obstacle detection: Obstacles can be detected using other means, such as ultrasound, but the car also needs to use its camera systems to identify any obstacles.
• Vehicle action recognition: The vehicle should know how to interact with other drivers since autonomous cars will drive alongside non-autonomous cars for many years to come.

The list of requirements goes on. Indeed, deep learning systems are compelling tools, but there are certain properties that can affect their practicality, particularly when it comes to autonomous cars. We will implement solutions for all of these problems in later chapters.