Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Shinsaku Kuwada

dblp:261/2900 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › network localization
cooperative localization
0.912025
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
0.912025
The Impact of Sensor Faults on Connected Autonomous Vehicle Localization · ICRA 2025
Vehicular, aerial and satellite networks
vehicular networks
0.912025
The Impact of Sensor Faults on Connected Autonomous Vehicle Localization · ICRA 2025
Smart cities and intelligent transportation
autonomous driving
0.312025
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
YearPublicationVenuePosition
2025 The Impact of Sensor Faults on Connected Autonomous Vehicle Localization
abstract
Connected 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
ICRA1