A self-driving car, also known as an autonomous vehicle (AV) is a vehicle that is capable of sensing its environment and moving safely with little or no human input. An AV is expected to excel in a few tasks classified into the following:
- The detection of an Object
- The Identification of an Object or recognition object classification
- The Object Localization and Prediction of Movement
- Driver Speech and Gesture Recognition
- Language Translation
In the autonomous car, one of the major tasks of a machine learning algorithm is the continuous rendering of the surrounding environment and forecasting the changes that are possible to these surroundings. It is built using the classification through data fusion from different external and internal sensors — like lidar, radars, cameras, or the IoT (Internet of Things).
AVs are built on the combination of supervised, unsupervised and reinforcement algorithms
Supervised Learning: 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
- Anomaly Detection
- Dimension reduction.
Unsupervised Learning: Unsupervised algorithms try to derive value from the available data. This implies, within the available data, an algorithm develops a relationship in order to detect the patterns or divides the data set into subgroups depending on the level of similarity between them. Unsupervised algorithms can be largely sub-categorized into association rule learning and clustering.
Source: Teradata — TheTree of Machine Learning Algorithms
Reinforcement Learning: The reinforcement algorithms are another set of machine learning algorithms that 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.
In particular, to AVs or Driverless Cars, the product is built basis the following the select few algorithms from supervised, unsupervised and reinforcement learnings as under:
- Regression algorithms
- Pattern recognition algorithms (classification)
- Cluster algorithms
- Decision matrix algorithms
Regression Algorithms: In Advanced Driver Assistance Systems (ADAS), images (radar or camera) play a very important role in localization and actuation, while the biggest challenge for any algorithm is to develop an image-based model for prediction and feature selection. The type of regression algorithms that can be used for self-driving cars is a Bayesian regression, neural network regression, and decision forest regression, among others.
Pattern Recognition Algorithms (Classification): In ADAS, the images obtained through sensors possess all types of environmental data; filtering of the images is required to recognize instances of an object category by ruling out the irrelevant data points. Pattern recognition algorithms are good at ruling out unusual data points. Recognition of patterns in a data set is an important step before classifying the objects. These types of algorithms can also be defined as data reduction algorithms.
These algorithms help in reducing the data set by detecting object edges and fitting line segments (polylines) and circular arcs to the edges. Line segments are aligned to edges up to a corner, then a new line segment is started. Circular arcs are fit to sequences of line segments that approximate an arc. The image features (line segments and circular arcs) are combined in various ways to form the features that are used for recognizing an object.
The support vector machines (SVM) with histograms of oriented gradients (HOG) and principal component analysis (PCA) are the most common recognition algorithms used in ADAS. The Bayes decision rule and K nearest neighbor (KNN) are also used.
Clustering: Sometimes the images obtained by the system are not clear and it is difficult to detect and locate objects. It is also possible that the classification algorithms may miss the object and fail to classify and report it to the system. The reason could be low-resolution images, very few data points or discontinuous data. This type of algorithm is good at discovering structure from data points. Like regression, it describes the class of problems and the class of methods. Clustering methods are typically organized by modeling approaches such as centroid-based and hierarchical. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality. The most commonly used type of algorithm is K-means, Multi-class Neural Network.
Decision Matrix Algorithms: This type of algorithm is good at systematically identifying, analyzing, and rating the performance of relationships between sets of values and information. These algorithms are mainly used for decision making. Whether a car needs to take a left turn or it needs to brake depends on the level of confidence the algorithms have on the classification, recognition, and prediction of the next movement of objects. These algorithms are models composed of multiple decision models independently trained and whose predictions are combined in some way to make the overall prediction while reducing the possibility of errors in decision making. The most commonly used algorithms are gradient boosting (GDM) and AdaBoosting.