In the real world, technologies influencing the impending success of automobiles are sensory cameras, radars, and LiDAR, allowing them to clearly assess speed, location, dimension, and more of the surrounding environment. They assist in detecting obscured objects at night through pulses of radar waves and identify the speed and location of the object. Moreover, these cars leverage inertial measurement units that control the acceleration and location of the vehicle.

Key Machine Learning Algorithms Used for Autonomous Driving

Machine learning in self-driving cars is a collaboration of several algorithms that contribute to the effective functioning of autonomous driving.

AdaBoost

AdaBoost is a fundamental algorithm used to enhance the learning process and performance of self-driving cars, so much so that it can eliminate the imperfections of machine learning. It combines the outputs of various low-tier algorithms to integrate a more effective algorithm for successful predictions and decision-making for automotives.

SIFT

SIFT (Scale Invariant Feature Transform) detects partially obscured objects by corresponding with its database. The algorithm allows image matching by allocating multiple points to innumerable objects, and these points assist the algorithm in recognizing the object. In essence, if a stationary vehicle is partially hidden behind a boulder, an autonomous vehicle scours its database through the points on the vehicle.

TextonBoost

Similar to AdaBoost, the TextonBoost algorithm encompasses several low-performing classifiers into a high-performing one to recognize objects accurately. It uses the context, shape and appearance of the object and identifies it through its characteristics.

YOLO

One of the best algorithms to identify and group objects, YOLO analyzes images by dividing them into segments. Each segment has bounding boxes and predictions to categorize the image.

 

We have only explored the tip of the iceberg with AI and ML technology, but self-driving cars are surely paving their way in the future.