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of 99
pro vyhledávání: '"Kim, BaekGyu"'
Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connecte
Externí odkaz:
http://arxiv.org/abs/2210.05034
Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dy
Externí odkaz:
http://arxiv.org/abs/2205.13770
Autor:
Park, Ji Gyo, Kim, BaekGyu, Song, Jin Yeong, Lee, Ho Kyoung, Kim, Min Chan, Hyun, Kyu, Shin, Da Seul, Lin, Zong-Hong, Choi, Dongwhi, Park, Sang Min
Publikováno v:
In Nano Energy 1 June 2024 124
Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share per
Externí odkaz:
http://arxiv.org/abs/2012.10252
Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation-intensive and
Externí odkaz:
http://arxiv.org/abs/2011.13075
Autor:
Wang, Haoxin, Liu, Tingting, Kim, BaekGyu, Lin, Chung-Wei, Shiraishi, Shinichi, Xie, Jiang, Han, Zhu
Publikováno v:
IEEE Communications Surveys & Tutorials, 2020
As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have at
Externí odkaz:
http://arxiv.org/abs/2009.12509
We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control policy (e.g. one trained as a neural network) is paired with a model-ba
Externí odkaz:
http://arxiv.org/abs/2008.07667
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Autor:
Sheng, Shili, Pakdamanian, Erfan, Han, Kyungtae, Kim, BaekGyu, Tiwari, Prashant, Kim, Inki, Feng, Lu
As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving. We designed and conducted a human s
Externí odkaz:
http://arxiv.org/abs/1904.11007
Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety implications and needs to be better understood. We consider the case study
Externí odkaz:
http://arxiv.org/abs/1904.07370