Weighted Branch Aggregation Based Deep Learning Model for Track Detection in Autonomous Racing
Published in The Second Tiny Papers Track at ICLR 2024, 2024
Intelligent track detection is a vital component of autonomous racing cars. We develop a novel Weighted Branch Aggregation based Convolutional Neural Network (WeBACNN) model that can accurately detect the track while being robust against image blurring due to high speed, and can work independently of lane markings.
Presented at the 12th International Conference on Learning Representations (ICLR 2024), May 7–11, 2024, Vienna, Austria.
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Recommended citation: Ghosh, S., Chen, Y. H., Huang, C. H., Jameel, A. S. M. M., El Gamal, A., & Labi, S. (2024). "Weighted branch aggregation based deep learning model for track detection in autonomous racing." The Second Tiny Papers Track at ICLR 2024.
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