Proposed frameworks for autonomous driving

In this section, we will discuss a proposed deep reinforcement learning framework for autonomous driving given by El Sallab et. al 2017 (https://arxiv.org/pdf/1704.02532.pdf). 

The following is an architecture of end-to-end deep neural networks: 

End to End training of Deep Neural Networks for Autonomous Driving by El Sallab et. al 2017 (https://arxiv.org/pdf/1704.02532.pdf)

Let's discuss the preceding architecture in detail. Inputs in this case are the aggregation of states of the environment over multiple timesteps. 

Spatial aggregation

The first unit of the architecture is the spatial aggregation network. It consists of two networks, each for the the following sub-processes:

• Sensor fusion
• Spatial features

The overall state includes the state of the vehicle as well as the state of the surrounding environment. The state of the vehicle includes position, geometric orientation, velocity, acceleration, current fuel left, current steering direction, and many...