Hierarchical Road Topology Learning for Urban Mapless Driving

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 th...

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Bibliographic Details
Main Authors: Zhang, Li, Tafazzoli, Faezeh, Krehl, Gunther, Xu, Runsheng, Rehfeld, Timo, Schier, Manuel, Seal, Arunava
Format: Conference Proceeding
Language:English
Subjects:
Behavioral sciences
Network topology
Planning
Predictive models
Roads
Scalability
Topology
Online Access:Request full text
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS47612.2022.9981820