Hierarchical Road Topology Learning for Urban Map-less Driving

Li Zhang; Faezeh Tafazzoli; Gunther Krehl; Runsheng Xu; Timo Rehfeld; Manuel Schier; Arunava Seal

Hierarchical Road Topology Learning for Urban Map-less Driving

Robotics 2021-04-02 v1 Computer Vision and Pattern Recognition Machine Learning

Authors: Li Zhang , Faezeh Tafazzoli , Gunther Krehl , Runsheng Xu , Timo Rehfeld , Manuel Schier , Arunava Seal

Abstract

The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area. Yet, this reliance is one of the obstacles to mass deployment of autonomous vehicles due to poor scalability of such prior maps. In this paper, we tackle the problem of online road map extraction via leveraging the sensory system aboard the vehicle itself. To this end, we design a structured model where a graph representation of the road network is generated in a hierarchical fashion within a fully convolutional network. The method is able to handle complex road topology and does not require a user in the loop.

Keywords

autonomous driving road topology learning hd maps graph representation fully convolutional network map-less driving

Cite

@article{arxiv.2104.00084,
  title  = {Hierarchical Road Topology Learning for Urban Map-less Driving},
  author = {Li Zhang and Faezeh Tafazzoli and Gunther Krehl and Runsheng Xu and Timo Rehfeld and Manuel Schier and Arunava Seal},
  journal= {arXiv preprint arXiv:2104.00084},
  year   = {2021}
}

Hierarchical Road Topology Learning for Urban Map-less Driving

Abstract

Keywords

Cite

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