One of the most remarkable applications of Machine Learning is the self-driving or autonomous car.
Machine Learning, combined with other disruptive technologies like IoT, is helping improve and enhance the functioning of autonomous cars. Thanks to ML, these autonomous cars are very much capable of sensing the environment around them and moving safely, requiring very little or no human intervention whatsoever.
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While ML is a crucial component of the centralized electronic control unit (ECU) in an autonomous car, efforts are being made to integrate ML even further in self-driving cars to shape them state-of-the-art creations. One of the primary functions of ML algorithms in an autonomous car is continuous monitoring of the surrounding environment and accurately predicting the possible changes to that surrounding. This core task can be further segmented.
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The Four Sub-Tasks
- Object detection
- Object identification/recognition
- Object localization
- Movement prediction
Self-driving cars usually incorporate numerous sensors that help them make sense of their surroundings, including GPS, radar, lidar, sonar, odometry, and inertial measurement units. They also have advanced control systems that can interpret sensory information to identify obstacles and figure out suitable navigation paths.
The ML-based applications that run an autonomous car’s infotainment system receive information from the sensor data fusion systems and make predictions accordingly. These algorithms can also integrate the driver’s gesture, speech recognition, and language translation in the car’s system.
Machine Learning Algorithms For Self-Driving Cars
1) Supervised ML algorithms
These algorithms use training dataset to learn. They keep learning until they reach the desired level that promises minimal errors. Supervised ML algorithms can further be categorized into classification, regression, and dimension reduction algorithms. For detailed information about the type of Machine Learning algorithms, read about Types of Machine Learning Algorithms
2) Unsupervised ML algorithms
These algorithms learn by making sense of the data at hand. No training datasets are used here. They try to find identifiable patterns within the data and then divide the data into classes/groups according to the level of similarity between them. Clustering and association rule learning are the two types of unsupervised ML algorithms.
Now, let’s dive into the inner workings of self-driving car algorithms. Machine Learning Algorithms for Autonomous Cars.
Machine Learning Algorithms for Autonomous Cars
Self-driving car Machine Learning algorithms are generally divided into four categories:
1) Regression Algorithms
Regression algorithms are used explicitly for predicting events. Bayesian regression, neural network regression, and decision forest regression are the three main types of regression algorithms used in self-driving cars.
In regression analysis, the relationship between two or more variables is estimated, and the effects of the variables are compared on different scales. Regression analysis is mainly dependent on three core metrics:
- The number of independent variables
- The type of dependent variables
- The shape of the regression line.
Regression algorithms use the repetitive aspects of an environment to form a statistical model of the relation between a particular image and the position of a specific object within the image. The statistical model can provide speedy online detection through image sampling. Gradually, it can extend to learn about other objects as well, without requiring substantial human intervention.
2) Pattern Recognition Algorithms (Classification)
Generally, the images obtained by the advanced driver-assistance systems (ADAS) are replete with an array of data from the surrounding environment. This data needs to be filtered to recognize the relevant images containing a specific category of objects. This is where pattern recognition algorithms enter.
Also known as data reduction algorithms, pattern recognition algorithms are designed to rule out unusual data points. Recognition of patterns in a data set is an essential step before classifying the objects.
These algorithms help in filtering the data obtained through the sensors by detecting object edges, and fitting line segments and circular arcs to the edges. Pattern recognition algorithms combine the line segments and circular arcs in many different ways to form the ultimate features for recognizing an object.
Support vector machines (SVM) with histograms of oriented gradients (HOG), principal component analysis (PCA), Bayes decision rule, and k-nearest neighbor (KNN) are some of the most commonly used pattern recognition algorithms in self-driving cars.
3) Cluster Algorithms
Cluster algorithms excel at discovering structure from data points. It may happen that the images obtained by the ADAS aren’t clear, or it may also occur that classification algorithms have missed identifying an object, thereby failing to classify and report it to the system.
This may happen due to the images being of very low-resolution or with very few data points. In such situations, it becomes difficult for the system to detect and locate objects in the surroundings.
Clustering algorithms define the class of problem and class of methods. Generally, clustering techniques are established using centroid-based and hierarchical modeling approaches. All clustering techniques focus on leveraging the inherent structures in the data to best organize the data into groups having the greatest commonality.
K-means and multi-class neural networks are the two most widely used clustering algorithms for autonomous cars.
4) Decision Matrix Algorithms
Decision matrix algorithms are essentially used for decision making. They are designed for systematically identifying, analyzing, and rating the performance of relationships between sets of values and information in them. The most widely used decision matrix algorithms in autonomous cars are gradient boosting (GDM) and AdaBoosting.
These algorithms determine the moves of the self-driving car. So, whether the car needs to take a left or a right turn, whether it needs to brake or accelerate, the answer to such questions is determined by the accuracy of these algorithms concerning classification, recognition, and prediction of the objects’ next movement.
Decision matrix algorithms comprise independently trained multiple decision models whose predictions are combined to generate the overall prediction while minimizing the possibility of errors.
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Conclusion
Together, all these ML algorithms go into the functioning of self-driving cars as we know it. At present, self-driving cars can perform the basic tasks of a human driver, such as controlling, navigating, and driving the vehicle, but of course, there are certain limitations to it as well. However, with further advancement of Machine Learning and improvement of self-driving car algorithms, we have a lot to look forward to from these autonomous cars.
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