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pro vyhledávání: '"Lin, Xuewu"'
The well-established modular autonomous driving system is decoupled into different standalone tasks, e.g. perception, prediction and planning, suffering from information loss and error accumulation across modules. In contrast, end-to-end paradigms un
Externí odkaz:
http://arxiv.org/abs/2405.19620
Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors. Many successful works have utilized mixture models which require identification of positive mixture components,
Externí odkaz:
http://arxiv.org/abs/2312.09501
In autonomous driving perception systems, 3D detection and tracking are the two fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D framework. We introduce two auxiliary training tasks (Temporal Instance Denoising
Externí odkaz:
http://arxiv.org/abs/2311.11722
Sparse algorithms offer great flexibility for multi-view temporal perception tasks. In this paper, we present an enhanced version of Sparse4D, in which we improve the temporal fusion module by implementing a recursive form of multi-frame feature samp
Externí odkaz:
http://arxiv.org/abs/2305.14018
Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performance, but still have lots of non-negligible merits. To push sparse 3D det
Externí odkaz:
http://arxiv.org/abs/2211.10581
Publikováno v:
In Heliyon 15 April 2024 10(7)
Multi-object tracking (MOT) has made great progress in recent years, but there are still some problems. Most MOT algorithms follow tracking-by-detection framework, which separates detection and tracking into two independent parts. Early tracking-by-d
Externí odkaz:
http://arxiv.org/abs/2103.12511
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Akademický článek
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Autor:
Xu, Qing, Lin, Xuewu, Cai, Mengchi, Guo, Yu-ang, Zhang, Chuang, Li, Kai, Li, Keqiang, Wang, Jianqiang, Cao, Dongpu
Publikováno v:
Chinese Journal of Mechanical Engineering; 11/20/2023, Vol. 36 Issue 1, p1-11, 11p