Is Deep Learning Really the Solution for Everything in Self-Driving Cars?

One of the biggest movements in the automotive industry now is the uprising of self-driving cars (autonomous vehicles, intelligent vehicles are some of the other names that are used). The degree to which the vehicle is given the freedom to drive autonomously is called Level of Automation. There are 5 levels in total starting from Level 0 (fully controlled by the driver), Level 1 (simple ADAS functions, such as steering control or acceleration), Level 2 (steering and acceleration can be controlled by the car, but the driver needs to be ready to take over). The best example of Level 2 automation is Tesla’s Autopilot. Level 3 automation still requires the driver to be present, but is able to completely shift "safety-critical functions" to the vehicle, under certain traffic or environmental conditions. Level 4 is considered to make the vehicle fully autonomous, up to some exceptional driving scenarios. Finally, Level 5 represents a fully autonomous self-driving car. 

Current state of technology already includes Level 2 (Tesla auto-pilot). Audi claims that it’s next A8 model will have Level 3 automation present ([1]). 

The higher the level of automation, the more “intelligent” the vehicle needs to become. Vehicle intelligence can be divided into 4 parts: sensing, perception, prediction and planning. In the sensing part, the data is gathered from the sensors, perception stage is responsible for using this information to create an understanding of the environment by means of computer vision. Prediction makes use of existing scenarios and training data to predict new outcomes and situations (such as the next movement of an object) and planning is the phase where the car makes purposeful decisions about its actions.

The last three of the four stages make use of machine learning.

Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. ([2])  

A special case of machine learning is deep learning, where the algorithms used mimic the human brain, by using so-called artificial neural networks (ANN). To quote the authors in [3]:

“From a layman’s standpoint, deep learning is a high performance, dynamic way of computerized decision-making that can learn features, objects, and patterns automatically and more accurately with the more (and better quality) data you give it. A deep learning system identifies and classifies patterns utilizing a set of analytical layers. The first layer does a relatively primitive task, such as identifying the edge of an image. It then sends the output to the next layer, which does a slightly more complex task, such as identifying the corner of the image. This process continues through each successive layer until every feature is identified. In the final, deepest layer, the system should reliably and quickly recognize the pattern.”

Deep learning is becoming very popular in the area of vehicle intelligence (especially in the progressive Silicon Valley circles) and some argue that this should be the preferred machine learning technique used in a fully intelligent, self-driving vehicle. In this article we discuss some of the current approaches in deep learning and review arguments for and against one of the main questions in the self-driving car industry currently discussed: “is deep learning really the solution for everything?”