The Machine Learning Algorithms Used in Self-Driving Cars

Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.

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.

The Supervised algorithms make use of a training dataset to learn and they continue to learn till they get to the level of confidence they aspire for (the minimization of the probability of error). The supervised algorithms can be sub-categorized into regression, classification and anomaly detection or dimension reduction.

The Unsupervised algorithms try to derive value from the available data. This implies, within the available data, an algorithm develops a relation in order to detect the patterns or divides the data set into subgroups depending on the level of similarity between them. The Unsupervised algorithms can be largely sub-categorized into association rule learning and clustering.

The reinforcement algorithms are another set of machine learning algorithms which fall between unsupervised and supervised learning. For each training example, there is a target label in supervised learning; there are no labels at all in unsupervised learning; the reinforcement learning consists of time-delayed and sparse labels – the future rewards.

The agent learns to behave in environment depending on these rewards. To understand the limitations and merits of an algorithm and to develop efficient learning algorithms is the goal in reinforcement learning. The reinforcement learning potentially addresses a huge number of practical applications that range from problems in AI to the control engineering or operations research – all that are relevant for the development of a self-driving car. This can be categorized as indirect learning and direct learning.

In the autonomous car, one of the major tasks of a machine learning algorithm is continuous rendering of surrounding environment and forecasting the changes that are possible to these surroundings. These tasks are classified into 4 sub-tasks:

The machine learning algorithms are loosely divided into 4 classes: decision matrix algorithms, cluster algorithms, pattern recognition algorithms and regression algorithms. One category of the machine learning algorithms can be utilized to accomplish 2 or more subtasks. For instance, the regression algorithms can be utilized for object localization as well as object detection or prediction of the movement.

The decision matrix algorithm systematically analyzes, identifies and rates the performance of relationships between the sets of information and values. These algorithms are majorly utilized for decision making. Whether a car needs to brake or take a left turn is based on the level of confidence these algorithms have on recognition, classification and prediction of the next movement of objects.