How is deep learning applied in self-driving cars?

A self-driving car (also called an autonomous/automated vehicle or driverless car) is a robotic vehicle that is capable of traveling between destinations and navigating without human intervention. To enable autonomy, self-driving cars detect and interpret environments using a variety of techniques such as radar, GPS and computer vision; and they then plan appropriate navigational paths to the desired destination.

In more detail, the following is how self-driving cars work in general:

  • The software plans the routes based on the destination, traffic, and road information and starts the car
  • A Light Detection and Ranging (LiDAR) sensor captures the surroundings in real time and creates a dynamic 3D map
  • Sensors monitor lateral movement to calculate the car's position on the 3D map
  • Radar systems exploit information on distances from other traffic participants, pedestrians, or obstacles
  • Computer vision algorithms recognize traffic signs, traffic lights, and other landmarks from a camera and provide advance notices
  • The algorithm-driven software analyzes all sensory data, combines inputs from other sources, and controls actions such as steering and braking, simulating the way humans perceive the surroundings and make decisions
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset