How Machine Learning is making Self-Driving Cars a Reality

In 2020, we saw advancements from companies like Waymo that allow customers to hail self-driving taxis, a service called Waymo One. Automotive Artificial Intelligence is rapidly enabling self-driving cars that use sensors to gather data about their surroundings. But how do self-driving cars interpret that data? This is the biggest use case of machine learning API in Automotive.

Driver less cars can identify objects, interpret situations, and make decisions based on object detection and object classification algorithms. They do this by detecting objects, classifying them, and interpreting what they are. The three major sensors used by self-driving cars work together as the human eyes and brain. These sensors are cameras, radar, and lidar. Together, they give the car a clear view of its environment. They help the car to identify the location, speed, and 3D shapes of objects that are close to it. Additionally, self-driving cars are now being built with inertial measurement units that monitor and control both acceleration and location.

How automotive Artificial Intelligence algorithms are used for self-driving cars:

To empower self-driving cars to make decisions, machine learning algorithms are trained based on real-life datasets.

Learn more about how Self Driven Cars behave and earn your Machine Learning API badge- use code 1q-ml-899 to get 9 credits, valid through 16th, May 21.