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

arXiv: https://arxiv.org/abs/2508.21271 · HTML: https://mlsanity.com/paper/2508.21271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019)
- [AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education](https://mlsanity.com/paper/2212.05241/) (2022)
- [Self-driving scale car trained by Deep reinforcement learning](https://mlsanity.com/paper/1909.03467/) (2019)
- [How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV](https://mlsanity.com/paper/1702.07600/) (2017)
- [Sim-To-Real Transfer for Miniature Autonomous Car Racing](https://mlsanity.com/paper/2011.05617/) (2020)
- [A software toolkit and hardware platform for investigating and comparing robot autonomy algorithms in simulation and reality](https://mlsanity.com/paper/2206.06537/) (2022)
- [UruBots Autonomous Cars Team One Description Paper for FIRA 2024](https://mlsanity.com/paper/2406.08745/) (2024)
- [Behavioral Cloning Models Reality Check for Autonomous Driving](https://mlsanity.com/paper/2409.07218/) (2024)
- [Behavior Cloning for Mini Autonomous Car Path Following](https://mlsanity.com/paper/2410.07209/) (2024)

## Abstract (arXiv)

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.