# Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments

## TL;DR

This paper investigates end-to-end deep learning approaches for autonomous vehicle control in simulated urban environments, demonstrating that temporal information improves driving performance.

## Contribution

It introduces two end-to-end architectures, including a recurrent model, and evaluates their effectiveness in urban driving scenarios with temporal dependencies.

## Key findings

- End-to-end systems can operate autonomously in simple urban environments.
- Utilizing temporal information enhances the system's ability to judge movement and distance.
- Recurrent architectures improve driving performance over traditional CNNs.

## Abstract

In recent years, considerable progress has been made towards a vehicle's ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a single, comprehensive software component. Recent breakthroughs in deep learning have significantly increased end-to-end systems' capabilities, and such systems are now considered a possible alternative to the current state-of-the-art solutions. This paper examines end-to-end learning for autonomous vehicles in simulated urban environments containing other vehicles, traffic lights, and speed limits. Furthermore, the paper explores end-to-end systems' ability to execute navigational commands and examines whether improved performance can be achieved by utilizing temporal dependencies between subsequent visual cues. Two end-to-end architectures are proposed: a traditional Convolutional Neural Network and an extended design combining a Convolutional Neural Network with a recurrent layer. The models are trained using expert driving data from a simulated urban setting, and are evaluated by their driving performance in an unseen simulated environment. The results of this paper indicate that end-to-end systems can operate autonomously in simple urban environments. Moreover, it is found that the exploitation of temporal information in subsequent images enhances a system's ability to judge movement and distance.