Automatic Building and Labeling of HD Maps with Deep Learning
This addresses the need for efficient infrastructure in autonomous driving by reducing human input and errors in map creation.
The paper tackles the problem of creating high-definition maps for autonomous vehicles by proposing a deep learning method that generates labeled HD maps from raw sensor data, resulting in highly accurate maps that speed up the process.
In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and labeling high-definition (HD) maps accurately and efficiently. Today, the process of creating HD maps requires a lot of human input, which takes time and is prone to errors. In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data. We implemented and tested our methods on several urban scenarios using data collected from our test vehicle. The results show that the pro-posed deep learning based method can produce highly accurate HD maps. This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.