A Racing Dataset and Baseline Model for Track Detection in Autonomous Racing
This provides a dataset and model for researchers in autonomous racing, addressing issues like blurriness and missing lane markings, but it is incremental as it builds on existing GAN techniques.
The authors tackled the lack of public datasets for track detection in autonomous racing by introducing RoRaTrack, a multi-camera image dataset from racing scenarios, and RaceGAN, a GAN-based baseline model that outperforms state-of-the-art methods.
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 https://github.com/ghosh64/RaceGAN.