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.
