VLDB 2026 Research / reviewers in the wild / expert
Oluwanifemi Adebayo Moses Adekanye
dblp:371/5635
· DBLP profile ↗
1ranked-venue papers
1as 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 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 |
Autonomous driving · 77% Generative modeling · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Autonomous driving › scenario generation
traffic scenario generation |
0.8 | 1 | 2024 | LLM-Powered Synthetic Environments for Self-Driving Scenarios · AAAI 2024 |
Methods — techniques the papers use, named apart from their topics
large language model · 0.8game engine · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | LLM-Powered Synthetic Environments for Self-Driving ScenariosabstractThis paper outlines a proposal exploring the potential use of Large Language Models (LLMs), particularly GPT-4, in crafting realistic synthetic environments for self-driving scenarios. The envisioned approach involves dynamic scene generation within game engines, leveraging LLMs to introduce challenging elements for autonomous vehicles. The proposed evaluation process outlines assessments such as realistic testing, safety metrics, and user interaction, aiming to set the stage for potential improvements in self-driving system performance. The paper aims to contribute to the AI field by discussing how LLMs could be utilized to create valuable testing grounds for autonomous vehicles, potentially fostering the development of more robust self-driving technology. The envisioned impact is the eventual enhancement of road safety and the possible acceleration of the adoption of autonomous vehicles, paving the way for a future with safer and more efficient transportation. Oluwanifemi Adebayo Moses Adekanye |
AAAI | 1 |