UruBots团队用深度学习让迷你电动车30秒内自动避障跑完赛道。
UruBots Autonomous Cars Challenge Pro Team Description Paper for FIRA 2025
- 用摄像头+CNN模型实时处理视觉信息，输出转向与油门控制
- 训练数据超1万张图像，车辆实测完成时间<30秒，速度约0.4米/秒
- 适合关注小型自动驾驶系统落地的工程师和竞赛参与者
本文介绍UruBots团队为2025年FIRA自主汽车挑战赛（专业组）开发的自动驾驶汽车。项目构建了一辆尺寸约等于遥控车的紧凑型电动车辆，可在不同赛道上实现自主导航。设计融合机械、电子组件及机器学习算法，使车辆基于摄像头获取的视觉输入实时决策。采用深度学习模型处理图像，通过包含超过一万个图像的数据集训练卷积神经网络（CNN），实现对车辆转向与油门的双重控制。测试中，车辆在30秒内完成赛道，平均速度约为0.4米/秒，并成功避开障碍物。
原文摘要 · Abstract (English)
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.
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