Hierarchical Road Topology Learning for Urban Map-less Driving

• URL: http://arxiv.org/abs/2104.00084v1
• Date: Wed, 31 Mar 2021 19:51:25 GMT
• Title: Hierarchical Road Topology Learning for Urban Map-less Driving
• Authors: Li Zhang, Faezeh Tafazzoli, Gunther Krehl, Runsheng Xu, Timo Rehfeld, Manuel Schier, Arunava Seal
• Abstract summary: We tackle the problem of online road map extraction via leveraging the sensory system aboard the vehicle itself. We design a structured model where a graph representation of the road network is generated in a hierarchical fashion within a fully convolutional network.
• Score: 8.107327095922729
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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