Year: 2025
Type: article
Abstract: ABSTRACT The most existing instance prediction methods often lack practical understanding and enhancement of bird's‐eye view (BEV) spatial‐temporal features, particularly distant features. To alleviate the above problems, we propose a robust BEV feature enhancement network (RO‐BEV). The proposed RO‐BEV includes two major components: the novel adaptive linearly increasing dividing (ALID) strategy and the hierarchical cross‐scale fusion (HCSF) module. Specifically, the ALID strategy can mitigate instance loss caused by insufficient features by providing a linearly increasing 3D representation to build a robust BEV feature. Then, the proposed HCSF module enhances spatial‐temporal BEV features in the latent space to predict vehicle instances by sampling from feature distributions at different scales and timestamps. Experimental results on the nuScenes dataset show that our RO‐BEV outperforms existing state‐of‐the‐art methods.
Source: Electronics Letters
DOI: 10.1049/ell2.70207
Authors: Zhi Yang, Tao Lu, Jiaming Wang, Xiujuan Lang, Shichang Fu, Jifeng Han
Institution: Wuhan Institute of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,590
OpenAlex ID: W4409342620