A Racing Dataset and Baseline Model for Track Detection in Autonomous Racing
Ghosh, Shreya, Chen, Yi-Huan, Huang, Ching-Hsiang, Jameel, Abu Shafin Mohammad Mahdee, Ho, Chien Chou, Gamal, Aly El, Labi, Samuel
–arXiv.org Artificial Intelligence
--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 A V-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. Modern vehicles are increasingly equipped with a range of computer vision technologies to assist drivers and improve road safety. A critical application of these technologies, particularly for autonomous and self-driving vehicles, is lane detection, which ensures that vehicles remain within designated lanes [1]. Lane detection systems not only help maintain proper lane alignment, but also provide visual cues to drivers about lane boundaries. Similarly, autonomous technologies are being integrated into race cars, giving rise to the emerging field of autonomous racing. In this domain, vehicles operate entirely without human intervention, relying solely on artificial intelligence and computer vision algorithms [2].
arXiv.org Artificial Intelligence
Feb-19-2025
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- North America > United States > Indiana (0.34)
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