Demo Data from VLDB 2026 / reviewers in the wild / expert
Shinsaku Kuwada
dblp:261/2900
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—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 2021Systems, architecture and hardware · 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.
| Computer networks
1 paper |
Vehicular, aerial and satellite networks · 33% Wireless sensing and localization · 33% Internet of things and sensor networks · 33% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Smart cities and intelligent transportation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization › network localization
cooperative localization |
0.9 | 1 | 2025 | The Impact of Sensor Faults on Connected Autonomous Vehicle Localization · ICRA 2025 |
Internet of things and sensor networks › wireless sensor network › network diagnosis
sensor fault detection |
0.9 | 1 | 2025 | The Impact of Sensor Faults on Connected Autonomous Vehicle Localization · ICRA 2025 |
Vehicular, aerial and satellite networks
vehicular networks |
0.9 | 1 | 2025 | The Impact of Sensor Faults on Connected Autonomous Vehicle Localization · ICRA 2025 |
Smart cities and intelligent transportation
autonomous driving |
0.3 | 1 | 2025 | The Impact of Sensor Faults on Connected Autonomous Vehicle Localization · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
integrity risk analysis · 1.7extended kalman filter · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | The Impact of Sensor Faults on Connected Autonomous Vehicle LocalizationabstractConnected autonomous vehicles (CAVs) can provide benefits over individual vehicles for precise navigation, especially in GNSS-denied environments. CAV collaboration can enhance estimation accuracy, but the safety of collaborative localization in the presence of undetected sensor faults remains underexplored. This paper introduces an integrity monitoring method for CAV collaborative localization in both centralized and decentralized implementations. Fault models for landmark and relative measurements are described, and the probability of hazardous misleading information, or integrity risk, is derived. Simulation and experimental results for notional two-CAV scenarios indicate that collaborative localization reduces integrity risk and enhances navigation safety. Shinsaku Kuwada, Mathieu Joerger, Matthew Spenko |
ICRA | 1 |