HRAN: Hierarchical Recurrent Attention Networks for Structured Online Maps

August 2020

tl;dr: Proposed the idea of polyline loss to encourage neural network to output structured polylines.

Overall impression

There are several works from Uber ATG that extracts polyline representation based on BEV maps.

This is one application of RNN in boundary extraction. Previous work include Polygon-RNN, Polygon-RNN++, Curve GCN also from Uber ATG. The main idea is to create a structured boundary to boost the efficiency for human-in-the-loop annotation.

One key difference from Polygon RNN is that polygon RCNN uses the cross entropy loss to learn the position fo the vertices. This is not ideal as there is no unique way to draw the same polygon.

Polyline Loss focuses on easier lane topology on highways, and DAGMapper focuses on highway driving, and focuses on hard cases like forks and merges. Polymapper only focuses on extracting road network and do not have lane-level information.

Key ideas

Technical details