Demo Data from EDBT 2026 / reviewers in the wild / expert
Steven Elliott
dblp:186/4891
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1
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 |
Motion planning and robot control · 61% Robot navigation and mapping · 30% Autonomous driving · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
model predictive control |
0.4 | 1 | 2019 | How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving · ICRA 2019 |
Robotics › Motion planning and robot control
motion planning |
0.4 | 1 | 2019 | How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving · ICRA 2019 |
Robotics › Robot navigation and mapping › social navigation
socially-aware path planning |
0.4 | 1 | 2019 | How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving · ICRA 2019 |
Robotics › Autonomous driving
interaction modeling |
0.1 | 1 | 2019 | How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
model predictive control · 0.4game theory · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful DrivingabstractWhile intelligence of autonomous vehicles (AVs) has significantly advanced in recent years, accidents involving AVs suggest that these autonomous systems lack gracefulness in driving when interacting with human drivers. In the setting of a two-player game, we propose model predictive control based on social gracefulness, which is measured by the discrepancy between the actions taken by the AV and those that could have been taken in favor of the human driver. We define social awareness as the ability of an agent to infer such favorable actions based on knowledge about the other agent's intent, and further show that empathy, i.e., the ability to understand others' intent by simultaneously inferring others' understanding of the agent's self intent, is critical to successful intent inference. Lastly, through an intersection case, we show that the proposed gracefulness objective allows an AV to learn more sophisticated behavior, such as passive-aggressive motions that gently force the other agent to yield. Steven Elliott, Yiwei Wang 0002, Yezhou Yang |
ICRA | 2 |
| 2015 | The CDS Collaborative: Goals, Deliverables, and Future Directions
Salvador Rodriguez-Loya, Emory Fry, Tadesse Sefer, Phillip B. Warner, Claude J. Nanjo, Jerry Goodnough, David Shields, Steven Elliott, Esteban Aliverti, Kensaku Kawamoto |
AMIA | 8 |