Demo Data from EDBT 2026 / reviewers in the wild / expert
Yeshu Jain
dblp:324/6191
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Robot navigation and mapping · 77% Planning, search and constraint satisfaction · 23% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › robot mapping
environment modeling |
0.6 | 1 | 2022 | DRG: A Dynamic Relation Graph for Unified Prior-Online Environment Modeling in Urban Autonomous Driving · ICRA 2022 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning › agent planning
action planning |
0.2 | 1 | 2022 | DRG: A Dynamic Relation Graph for Unified Prior-Online Environment Modeling in Urban Autonomous Driving · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
relational mapping · 0.6graph-based modeling · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | DRG: A Dynamic Relation Graph for Unified Prior-Online Environment Modeling in Urban Autonomous DrivingabstractEnvironment 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. Rowan Dempster, Mohammad K. Al-Sharman, Yeshu Jain, Jeffery Li, Derek Rayside, William W. Melek |
ICRA | 3 |