Zobrazeno 1 - 10
of 203
pro vyhledávání: '"Eijiro TAKEUCHI"'
Autor:
Yuki Kitsukawa, Tatsuya Minami, Yudai Yamazaki, Junich Meguro, Eijiro Takeuchi, Yoshiki Ninomiya, Shinpei Kato, Masato Edahiro
Publikováno v:
International Journal of Automotive Engineering, Vol 13, Iss 4, Pp 206-213 (2022)
ABSTRACT: Ego-vehicle localization is a critical technology in autonomous driving systems, and one of the widely used methods for localization is scan matching between a 3D map and real-time LiDAR scan. This method is known to fail due to factors suc
Externí odkaz:
https://doaj.org/article/c0d58a8a1263421fadae6c6ccfe26b8b
Publikováno v:
IEEE Access, Vol 10, Pp 105734-105743 (2022)
In recent decades, many learning-based autonomous driving systems have been proposed, and researchers have also created toolkits for developing these systems. These toolkits allow developers to train their models easily, and then test them using simu
Externí odkaz:
https://doaj.org/article/fb8d32093ae5448984d9af86529908df
Publikováno v:
IEEE Access, Vol 10, Pp 57759-57782 (2022)
As the operational domain of autonomous vehicles expands, encountering occlusions during navigation becomes unavoidable. Most of the existing research on occlusion-aware motion planning focuses only on the longitudinal motion of the ego vehicle and n
Externí odkaz:
https://doaj.org/article/c970cc11e3b9468fa8a6b2e8223281c4
Autor:
Jacob Lambert, Alexander Carballo, Abraham Monrroy Cano, Patiphon Narksri, David Wong, Eijiro Takeuchi, Kazuya Takeda
Publikováno v:
IEEE Access, Vol 8, Pp 131699-131722 (2020)
Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers rel
Externí odkaz:
https://doaj.org/article/4400b78780114b42880619de937fc4a4
Publikováno v:
International Journal of Automotive Engineering, Vol 10, Iss 4, Pp 299-308 (2019)
Estimating the intentions and trajectories of other vehicles is critical to achieving stable, long-term planning and decision making in autonomous driving systems. This paper introduces a novel technique for estimating the intention and trajectory pr
Externí odkaz:
https://doaj.org/article/73cbcc8b317a49e0bc7703adea7d643e
Publikováno v:
IEEE Access, Vol 7, Pp 113616-113625 (2019)
Point cloud data from LiDAR sensors is the currently the basis of most L4 autonomous driving systems. Sharing and storing point clouds will also be important for future applications, such as accident investigation or V2V/V2X networks. Due to the huge
Externí odkaz:
https://doaj.org/article/e6f1d3226a174036b160025f709d45a4
Publikováno v:
Journal of Robotics and Mechatronics. 35:435-444
To realize autonomous vehicle safety, it is important to accurately estimate the vehicle’s pose. As one of the localization techniques, 3D point cloud registration is commonly used. However, pose errors are likely to occur when there are few featur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a178d413a1b5ccf0d317ff9d9ca04ee4
https://doi.org/10.20965/jrm.2023.p0435
https://doi.org/10.20965/jrm.2023.p0435
Publikováno v:
Nihon Kikai Gakkai ronbunshu, Vol 86, Iss 892, Pp 20-00151-20-00151 (2020)
Localization in autonomous vehicles is an important technology, and the use of 3D point clouds, which provide accurate information on the road surroundings, has been attracting attention to help improve localization. In recent years, many methods for
Externí odkaz:
https://doaj.org/article/69ccfb8f1bd04a90a1df6288d0f9bbfb
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 22:243-256
Continuous point cloud data is being used more and more widely in practical applications such as mapping, localization and object detection in autonomous driving systems, but due to the huge volume of data involved, sharing and storing this data is c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd6f8e29f837c5424e180d791daa74f6
https://doi.org/10.1109/tits.2019.2956066
https://doi.org/10.1109/tits.2019.2956066
Autor:
Naoki Akai, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda, Yoichi Morales, Hailong Liu, Kyle Sama
Publikováno v:
IEEE Transactions on Vehicular Technology. 69:9315-9329
This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors. Deep learning techniques were used to extract latent features from the collected
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e17fe4ce9022641d0a2158524ebffc32
https://doi.org/10.1109/tvt.2020.2980197
https://doi.org/10.1109/tvt.2020.2980197