Computer Vision is a key technology for building algorithms to enable self-driving cars. One of the pioneering projects in this field was an experimental system called PilotNet  by Nvidia. It uses a deep neural network (DNN) that takes image frames from cameras mounted on the front of a car and determines the trajectory (steering angle) that is to be applied to the steering wheel.

PilotNet’s architecture is composed of the layers shown in Figure 1:

Conceptual Image Illustrating the Layers of the PilotNet Model

Figure 1: Conceptual Image Illustrating the Layers of the PilotNet Model — Image source: NVIDIA.

In a nutshell, input in the form of images from the cameras are transformed using a series of convolution layers to extract features. Fully connected layers are then used to output a single angle that the model believes the car’s steering wheel should be turned in order to successfully navigate. Of course if you were to try and build this model with conventional tools (e.g., in TensorFlow) it would be difficult to visualize this architecture. This is where the PerceptiLabs visual modeling tool really shines, as it allows you to see the model as you build it.

With research into self-driving cars accelerating (pun intended) we thought why not recreate the PilotNet model in PerceptiLabs to show just how easy it is to build. Then to prove the point, we decided to do it in front of a live audience! Here’s what happened:

#machine-learning #visualization #nvidia #tensorflow #self-driving-cars

Recreating Nvidia’s PilotNet in PerceptiLabs
1.45 GEEK