RoRaTrack

A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC). RoRaTrack addresses common problems such as blurriness due to high speed, color inversion from the camera, and absence of lane markings on the track. Consequently, we propose RaceGAN, a baseline model based on a Generative Adversarial Network (GAN) that effectively addresses these challenges. The proposed model demonstrates superior performance compared to current state-of-the-art machine learning models in track detection. The dataset and code for this work are available at github.com/RaceGAN.赛车相关研究领域面临的一项重大挑战，是缺乏面向下游任务、带有对应标注的公开原始图像数据集。本文提出全新数据集RoRaTrack，其包含源自赛车场景的多摄像头标注图像数据，可用于赛道检测任务。本数据集由我们与印第安纳波利斯自动驾驶挑战赛（Indy Autonomous Challenge, IAC）合作，在印第安纳州某赛车场的达拉拉AV-21赛车上采集得到。RoRaTrack针对性解决了赛车场景下的常见数据问题：包括高速运动导致的图像模糊、摄像头引发的色彩反转，以及赛道车道标线缺失等问题。为此，我们提出了基于生成对抗网络（Generative Adversarial Network, GAN）的基线模型RaceGAN，可有效应对上述各类挑战。经实验验证，该模型在赛道检测任务上的表现优于当前最优的机器学习模型。本研究相关的数据集与代码已开源至github.com/RaceGAN。

搜集汇总

背景与挑战

背景概述

RoRaTrack是一个用于自动驾驶赛车赛道检测的数据集，包含1,398张从印第安纳州赛车赛道收集的多摄像头图像及其对应的标签掩码。该数据集专门设计用于解决高速场景下的图像模糊、颜色反转和缺乏车道标记等挑战，为训练和评估机器学习模型提供了资源，并附带了训练和测试分割文件。