Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yrjar Gedde
dblp:371/5518
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 100% | |
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies › skill acquisition
driving skill training |
0.8 | 1 | 2024 | A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill Level · AAAI 2024 |
Learning and educational technologies
personalized learning |
0.8 | 1 | 2024 | A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill Level · AAAI 2024 |
Virtual and augmented reality › virtual environment
driving simulation |
0.2 | 1 | 2024 | A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill Level · AAAI 2024 |
Methods — techniques the papers use, named apart from their topics
case study · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill LevelabstractCurrently, students acquire driving skills by practicing in actual traffic conditions and through direct interactions with an instructor. While one-on-one interactions could be tailored to a student’s learning style and skill level, making them effective for learning, one-on-one interactions are also inefficient, potentially costly, and not standardized with limitations on which traffic situation can be safely taught. For these exact reasons Way AS has developed and commercially deployed a virtual driving instructor that educates students in high-fidelity simulators. In this paper, we present a module, the Lesson generator, that extends the virtual driving instructor to generate personalized lessons for individual students with the goal to practice in a focused and deliberately fashion the skills that need practice for the students to become proficient drivers. A case study is presented, and the path to deployment is discussed. J. Fredrik R. Bjørnland, Yrjar Gedde, Johannes Rehm, Irina Reshodko, Odd Erik Gundersen |
AAAI | 2 |