Zobrazeno 1 - 10
of 214
pro vyhledávání: '"Shunsuke Kamijo"'
Publikováno v:
IEEE Access, Vol 12, Pp 138121-138133 (2024)
This paper introduces Scenario-Risk Net, a novel approach to integrate risk assessment into the autonomous driving system segmentation process. The method incorporates an attention-based layer into an existing segmentation network, enabling the class
Externí odkaz:
https://doaj.org/article/ba94ae0b48ad4ce79c15b019f98570c7
Publikováno v:
IEEE Access, Vol 12, Pp 13088-13099 (2024)
This paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the e
Externí odkaz:
https://doaj.org/article/bd418eed3e8641f881e4cac2d4ac2f84
Autor:
Yuki Endo, Shunsuke Kamijo
Publikováno v:
IEEE Access, Vol 12, Pp 9321-9330 (2024)
Lane-level self-localization is a critical task in the field of autonomous driving. Map-based self-localization is commonly employed to achieve lane-level accuracy in urban settings. However, it is known that in certain locations, such as narrow road
Externí odkaz:
https://doaj.org/article/32a819496aef43d58a41ab114a678930
Autor:
Jinho Lee, Daiki Shiotsuka, Geonkyu Bang, Yuki Endo, Toshiaki Nishimori, Kenta Nakao, Shunsuke Kamijo
Publikováno v:
IATSS Research, Vol 47, Iss 2, Pp 251-262 (2023)
Recently, autonomous driving technologies require robust perception performance through deep learning with huge data and annotations. To guarantee performance, perception accuracy should be robust even in nighttime. However, lots of perception techno
Externí odkaz:
https://doaj.org/article/235b93085a7848b1a8f22abc0ec6d915
Autor:
Tetsuya Manabe, Kazuo Mizuno, Keisuke Hatano, Masahiko Kaneko, Mai Inoue, Masatoshi Nomura, Shunsuke Kamijo
Publikováno v:
IATSS Research, Vol 47, Iss 1, Pp 35-43 (2023)
In this study, we develop a system to provide information on the sterilization of baggage carts and arriving passenger baggage to airport (Hereafter referred as arrival baggage) by using ultraviolet (UV) sterilization and information communication te
Externí odkaz:
https://doaj.org/article/82d0b5890e464a7ebd0a28dad3d49b4c
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 4, Pp 833-845 (2023)
Recently, research on autonomous driving has focused on the advent of various deep learning algorithms. The main sensors for autonomous driving include cameras, LiDAR, and radar, but these algorithms primarily focus on image and LiDAR data. This is b
Externí odkaz:
https://doaj.org/article/306f0a824470480fa8ef2ea5ebc6b4ee
Publikováno v:
IATSS Research, Vol 46, Iss 4, Pp 450-456 (2022)
Map-based self-localization estimates the pose of the self-driving vehicle in an environment, becoming an essential part of autonomous driving tasks. Generally, maps used in self-localization have detailed geometric information on an environment in f
Externí odkaz:
https://doaj.org/article/b933c2f9bc754c8a92adedbcb3c0948e
Publikováno v:
Sensors, Vol 24, Iss 4, p 1339 (2024)
Autonomous driving systems heavily depend on perception tasks for optimal performance. However, the prevailing datasets are primarily focused on scenarios with clear visibility (i.e., sunny and daytime). This concentration poses challenges in trainin
Externí odkaz:
https://doaj.org/article/98bae54eb7e84127b6a24638969a3381
Publikováno v:
Sensors, Vol 24, Iss 2, p 559 (2024)
In autonomous vehicles, the LiDAR and radar sensors are indispensable components for measuring distances to objects. While deep-learning-based algorithms for LiDAR sensors have been extensively proposed, the same cannot be said for radar sensors. LiD
Externí odkaz:
https://doaj.org/article/152e644165954b0dba51b17cffaf4795
Publikováno v:
International Journal of Automotive Engineering, Vol 12, Iss 4, Pp 125-133 (2021)
Understanding the level of environmental risk using vehicle-mounted camera traffic scenes is useful in advanced driver assistance systems (ADAS) to improve vehicle safety. We propose a fast, memory-efficient computer vision based environmental risk p
Externí odkaz:
https://doaj.org/article/1d3db9f5c7924d349ba73c5c33a34b7f