DRG: A Dynamic Relation Graph for Unified Prior-Online Environment Modeling in Urban Autonomous Driving
Rowan Dempster, Mohammad Al-Sharman, Yeshu Jain, Jeffery Li, Derek Rayside, William Melek
Abstract
Environment modeling is the backbone of how autonomous agents understand the world, and therefore has significant implications for decision-making and verification. Motivated by the success of relational mapping tools such as Lanelet2, we present the Dynamic Relation Graph (DRG). The DRG is a novel method for extending prior relational maps to include online observations, creating a unified en-vironment model which incorporates both prior and online data sources. Our prototype implementation models a finite set of heterogeneous features including road signage and pedestrian movement. However, the methodology behind the DRG can be expanded to a wider range of features in a fashion that does not increase the complexity of behavioral planning. Simulated stress tests indicate the DRG's effectiveness in decreasing decision-making complexity, and deployment on the University of Waterloo's WATonomous research vehicle demonstrates its practical utility. The prototype code will be released at github.com/WATonomous/DRG.
BibTeX
@inproceedings{icra2022_drgadynamicrelat,
title = {DRG: A Dynamic Relation Graph for Unified Prior-Online Environment Modeling in Urban Autonomous Driving},
author = {Rowan Dempster and Mohammad Al-Sharman and Yeshu Jain and Jeffery Li and Derek Rayside and William Melek},
booktitle = {ICRA 2022},
year = {2022}
}