Zobrazeno 1 - 10
of 698
pro vyhledávání: '"Lu, Hongsheng"'
Trajectory prediction is fundamental in computer vision and autonomous driving, particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observation
Externí odkaz:
http://arxiv.org/abs/2404.02227
Autor:
Qu, Deyuan, Chen, Qi, Bai, Tianyu, Lu, Hongsheng, Fan, Heng, Zhang, Hao, Fu, Song, Yang, Qing
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles. However, the absence of feature maps shared from other vehicles can lead to a significant decline in 3
Externí odkaz:
http://arxiv.org/abs/2312.04822
Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modal
Externí odkaz:
http://arxiv.org/abs/2310.06138
Autor:
Abbasian, Mahyar, Rajabzadeh, Taha, Moradipari, Ahmadreza, Aqajari, Seyed Amir Hossein, Lu, Hongsheng, Rahmani, Amir
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets. However, the challenge of exerting control over the generation process of GANs remains a significant hurdle. In this
Externí odkaz:
http://arxiv.org/abs/2307.13978
One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position. These relationships can be
Externí odkaz:
http://arxiv.org/abs/2307.00167
Correspondence identification (CoID) is an essential component for collaborative perception in multi-robot systems, such as connected autonomous vehicles. The goal of CoID is to identify the correspondence of objects observed by multiple robots in th
Externí odkaz:
http://arxiv.org/abs/2303.07555
In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fu
Externí odkaz:
http://arxiv.org/abs/2211.12021
High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can, however, become a bottleneck when high accuracy localization is requi
Externí odkaz:
http://arxiv.org/abs/2204.01510
Autor:
Fischer, Moritz Benedikt, Dörner, Sebastian, Cammerer, Sebastian, Shimizu, Takayuki, Lu, Hongsheng, Brink, Stephan ten
We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel conditions. This online adaptation via retraining is mainly motivated by t
Externí odkaz:
http://arxiv.org/abs/2203.13571
Publikováno v:
In Colloids and Surfaces A: Physicochemical and Engineering Aspects 20 November 2024 703 Part 2
