News Flash

Self-Driving Cars

Deep-learning plays a vital role in the development of Autonomous Vehicles. With Convolutional Neural Networks at the heart of vision tasks, objects are recognized and their placement mapped into the ‘mind’ of the car. Lidar is used to capture the environment around the vehicle and then instantaneously recognized by putting multiple views through a previously trained visual model. 

Look at the articles cited below to better understand the science of this and how complex the process is. 

In case you have not seen this video, imagine if all (but the Motor Cycle) vehicles were autonomous.  I guess you could try navigating this intersection.



How do Self-Driving Cars See?

Good discussion of the fundamental principles behind Autonomous Vehicle vision.

Deep Learning for Self-Driving Cars

Serious investigation into Convolutional Neural Networks using Pytorch to train the model.

The author mentions using Colab free GPUs to train the model and mentions the

Udacity free Deep-Learning Course.

The Driverless Technology of Things — A Disruption Ready To Happen

A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way