# Mini Autonomous Car Driving based on 3D Convolutional Neural Networks

## TL;DR

This paper introduces a 3D CNN-based methodology for mini autonomous car driving using RGB-D data, evaluated in simulation, showing promising results over RNNs in control and generalization.

## Contribution

The work presents a novel application of 3D CNNs for autonomous driving in mini cars, enabling efficient evaluation and comparison in simulated environments.

## Key findings

- 3D CNNs outperform RNNs in certain driving tasks
- Architectural modifications impact model generalization
- Track complexity affects control performance

## Abstract

Autonomous driving applications have become increasingly relevant in the automotive industry due to their potential to enhance vehicle safety, efficiency, and user experience, thereby meeting the growing demand for sophisticated driving assistance features. However, the development of reliable and trustworthy autonomous systems poses challenges such as high complexity, prolonged training periods, and intrinsic levels of uncertainty. Mini Autonomous Cars (MACs) are used as a practical testbed, enabling validation of autonomous control methodologies on small-scale setups. This simplified and cost-effective environment facilitates rapid evaluation and comparison of machine learning models, which is particularly useful for algorithms requiring online training. To address these challenges, this work presents a methodology based on RGB-D information and three-dimensional convolutional neural networks (3D CNNs) for MAC autonomous driving in simulated environments. We evaluate the proposed approach against recurrent neural networks (RNNs), with architectures trained and tested on two simulated tracks with distinct environmental features. Performance was assessed using task completion success, lap-time metrics, and driving consistency. Results highlight how architectural modifications and track complexity influence the models' generalization capability and vehicle control performance. The proposed 3D CNN demonstrated promising results when compared with RNNs.