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J. Davis

dblp:24/7387 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%
Artificial intelligence
1 paper
Motion planning and robot control · 50% Autonomous driving · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
human-in-the-loop control
0.112010
The Effects of Time Lag on Driving Performance and a Possible Mitigation · IEEE Trans. Robotics 2010
Human-robot interaction
latency effects
0.112010
The Effects of Time Lag on Driving Performance and a Possible Mitigation · IEEE Trans. Robotics 2010
Human-robot interaction › teleoperation › time-delayed teleoperation
predictive display
0.112010
The Effects of Time Lag on Driving Performance and a Possible Mitigation · IEEE Trans. Robotics 2010
Robotics › Autonomous driving › simulation
driving simulation
0.012010
The Effects of Time Lag on Driving Performance and a Possible Mitigation · IEEE Trans. Robotics 2010
Robotics › Motion planning and robot control
teleoperation
0.012010
The Effects of Time Lag on Driving Performance and a Possible Mitigation · IEEE Trans. Robotics 2010

Methods — techniques the papers use, named apart from their topics

predictive display · 0.2
YearPublicationVenuePosition
2010 The Effects of Time Lag on Driving Performance and a Possible Mitigation
abstract
Fixed time lags within control systems are known to reduce the speed and accuracy of human-control actions. Further, the effects of variable time lags are not well-studied or understood but may exacerbate the negative effects associated with fixed time lags. Several studies have demonstrated mechanisms designed to combat the effects of time lag, which include adaptation, mathematical predictors and filters, and predictive displays. This experiment examined the effects of both fixed and variable time lag on a simulated-indirect-vision-driving task, as well as a possible mitigation (predictive display) for these effects. Results revealed that variable time lag significantly increased average lane offset more than fixed time lag, which indicates a decrease in driver accuracy. A predictive display significantly reduced lane offset and increased vehicle speed for both fixed and variable time lags. The predictive display also resulted in lower reports of operator workload. These results revealed the negative performance affects of variable time lag and demonstrated the utility of a predictive display to overcome the negative performance effects associated with fixed and variable time lags.
J. Davis, C. Smyth, Kaleb McDowell
IEEE Trans. Robotics1