Weighted Branch Aggregation Based Deep Learning Model for Track Detection in Autonomous Racing
Shreya Ghosh ⋅ Yi-Huan Chen ⋅ Ching-Hsiang Huang ⋅ Abu Shafin Mohammad Mahdee Jameel ⋅ Aly El Gamal ⋅ Samuel Labi
Abstract
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. The code and dataset for this work is available at (anonymous).
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