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Steven Elliott

dblp:186/4891 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
model predictive control
0.412019
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
0.412019
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
0.412019
How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving · ICRA 2019
Robotics › Autonomous driving
interaction modeling
0.112019
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
YearPublicationVenuePosition
2019 How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving
abstract
While 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
ICRA2
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
AMIA8