Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Kyongsu Yi"'
Publikováno v:
IEEE Access, Vol 12, Pp 154526-154534 (2024)
This paper proposes the Temporal Feature Extraction Detector (TFEdet), a novel deep learning-based 3D multi-frame object detector efficiently utilizing temporal features from consecutive point clouds. To leverage previously processed frames, inter-fr
Externí odkaz:
https://doaj.org/article/1981abdb49504d51a902abc6e6c5be77
Publikováno v:
IEEE Access, Vol 12, Pp 11743-11760 (2024)
This study proposes a novel motion planning strategy to address localization uncertainty in autonomous buses. Conventional motion planning algorithms utilize information from high-definition (HD) maps to overcome the limited detection range of on-boa
Externí odkaz:
https://doaj.org/article/7044c70f9c7243488df928b65c8f65df
Publikováno v:
IEEE Access, Vol 11, Pp 104554-104567 (2023)
Predicting the future trajectories of surrounding pedestrians is undoubtedly one of the most essential but challenging tasks for safe urban autonomous driving. Despite this importance, there has been limited research conducted on the egocentric view
Externí odkaz:
https://doaj.org/article/33c75997d30f4e93a5f8b210b9879d75
Publikováno v:
IEEE Access, Vol 11, Pp 5772-5788 (2023)
This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes includin
Externí odkaz:
https://doaj.org/article/1c97b3b3b1f542658ae1d68bee88dc3f
Publikováno v:
IEEE Access, Vol 10, Pp 27014-27030 (2022)
This paper presents an emergency pullover algorithm for fail-safe systems designed for level-4 autonomous vehicles. The proposed algorithm utilizes feedback gain adaptation, based on sensitivity estimation, and cost-based learning. Vehicle failure wi
Externí odkaz:
https://doaj.org/article/d932d0fda5d14f6eb4a9829d1faf446c
Publikováno v:
IEEE Access, Vol 10, Pp 27863-27880 (2022)
This paper presents a sliding mode-based adaptive fault detection and emergency control algorithm for implementation in fail-safe systems of autonomous vehicles. The overall algorithm is comprised of a fault detection part and a fail-safe control par
Externí odkaz:
https://doaj.org/article/bd6315805c624d89abf2a29abeed2a6c
Publikováno v:
IEEE Access, Vol 9, Pp 63440-63455 (2021)
This paper presents a probabilistic trajectory prediction of cut-in vehicles exploiting the information of interacting vehicles. First, a probability distribution of behavioral parameters, which represents the characteristics of lane-change motion, i
Externí odkaz:
https://doaj.org/article/9cb365b97bb64fbd8bbf0e7cf4c1f2ce
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 1, Pp 2-14 (2020)
This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surroundi
Externí odkaz:
https://doaj.org/article/5e6b7a531ae4491082dac95ee3165634
Autor:
Heungseok Chae, Kyongsu Yi
Publikováno v:
IEEE Access, Vol 8, Pp 51363-51376 (2020)
This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must
Externí odkaz:
https://doaj.org/article/26e11910c4e14ea4b2e0a8b3a4afeaa0
Autor:
Yonghwan Jeong, Kyongsu Yi
Publikováno v:
IEEE Access, Vol 8, Pp 106183-106197 (2020)
This paper presents an interactive motion predictor to infer the intention of cut-in vehicles using a bidirectional long short-term memory (Bi-LSTM) module. The proposed predictor consists of three modules: maneuver recognition, trajectory prediction
Externí odkaz:
https://doaj.org/article/50769d9ee7714ca0bcc1b82c28d433ca