Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Keisuke Yoneda"'
Publikováno v:
International Journal of Automotive Engineering, Vol 14, Iss 1, Pp 20-26 (2023)
ABSTRACT: Lane line is one of the important environmental information used in various self-localization methods. However, the effect of lane lines fading or wears overtime on localization accuracy is not clear. This paper investigates effects of road
Externí odkaz:
https://doaj.org/article/dc9ad39132d0401ab8d5ab1bcbf87e71
Publikováno v:
Sensors, Vol 23, Iss 20, p 8367 (2023)
Recognition of surrounding objects is crucial for ensuring the safety of automated driving systems. In the realm of 3D object recognition through deep learning, several methods incorporate the fusion of Light Detection and Ranging (LiDAR) and camera
Externí odkaz:
https://doaj.org/article/590959d8b69544288b8274155f64a738
Publikováno v:
IATSS Research, Vol 43, Iss 4, Pp 253-262 (2019)
During automated driving in urban areas, decisions must be made while recognizing the surrounding environment using sensors such as camera, Light Detection and Ranging (LiDAR), millimeter-wave radar (MWR), and the global navigation satellite system (
Externí odkaz:
https://doaj.org/article/81d2017d8c594393a18168d38ba6d3d8
Publikováno v:
Remote Sensing, Vol 14, Iss 16, p 4058 (2022)
This paper demonstrates the weakness of GNSS/INS-RTK (GIR) systems in mapping challenging environments because of obstruction and deflection of satellite signals. Thus, it emphasizes that the strategy of mapping companies to commercially provide maps
Externí odkaz:
https://doaj.org/article/704dccf56692428596d627152a419e9e
Publikováno v:
Sensors, Vol 22, Iss 9, p 3545 (2022)
Localization is an important technology for autonomous driving. Map-matching using road surface pattern features gives accurate position estimation and has been used in autonomous driving tests on public roads. To provide highly safe autonomous drivi
Externí odkaz:
https://doaj.org/article/b4c42040a67c4c51b6a4a9aefbaea2c0
Publikováno v:
International Journal of Automotive Engineering, Vol 9, Iss 4, Pp 195-201 (2018)
This paper proposes a self-localization method for automated vehicles by using traffic signs. The proposed method aims to improve the accuracy of longitudinal self-localization. In order to search for the highest probability position, map matching is
Externí odkaz:
https://doaj.org/article/074720ec49e7459c93fecb495dbfd676
Publikováno v:
Sensors, Vol 20, Iss 4, p 1181 (2020)
Traffic light recognition is an indispensable elemental technology for automated driving in urban areas. In this study, we propose an algorithm that recognizes traffic lights and arrow lights by image processing using the digital map and precise vehi
Externí odkaz:
https://doaj.org/article/96eafd4db8d54318b87edd510242f6ad
Publikováno v:
Artificial Life and Robotics. 28:343-351
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0adc455ca31eee37485e5dac01ab9615
https://doi.org/10.1007/s10015-023-00863-1
https://doi.org/10.1007/s10015-023-00863-1
Autor:
Keisuke Yoneda, Naoki Suganuma
Publikováno v:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. :763-769
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5fb8d31206ad4140a2ffb0e767e9809
https://doi.org/10.1587/transfun.2021wbi0002
https://doi.org/10.1587/transfun.2021wbi0002
Publikováno v:
Proceedings of the 15th International Conference on Agents and Artificial Intelligence.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56a96a63f3311279ff5e742126c24905
https://doi.org/10.5220/0011645000003393
https://doi.org/10.5220/0011645000003393