UruBots Autonomous Cars Challenge Pro Team Description Paper for FIRA 2025

TL;DR

This paper details the development of an autonomous RC-scale vehicle using deep learning for real-time navigation in a competitive challenge, demonstrating effective obstacle avoidance and rapid track completion.

Contribution

The paper introduces a compact autonomous vehicle utilizing CNN-based visual processing and real-time control for competitive autonomous driving.

Findings

Vehicle completed track in under 30 seconds

Achieved obstacle avoidance during navigation

Trained CNN with over ten thousand images

Abstract

This paper describes the development of an autonomous car by the UruBots team for the 2025 FIRA Autonomous Cars Challenge (Pro). The project involves constructing a compact electric vehicle, approximately the size of an RC car, capable of autonomous navigation through different tracks. The design incorporates mechanical and electronic components and machine learning algorithms that enable the vehicle to make real-time navigation decisions based on visual input from a camera. We use deep learning models to process camera images and control vehicle movements. Using a dataset of over ten thousand images, we trained a Convolutional Neural Network (CNN) to drive the vehicle effectively, through two outputs, steering and throttle. The car completed the track in under 30 seconds, achieving a pace of approximately 0.4 meters per second while avoiding obstacles.