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Unveiling the Black Box: Independent Functional Module Evaluation for Bird's-Eye-View Perception Model

Autor: Zhang, Ludan, Ding, Xiaokang, Dai, Yuqi, He, Lei, Li, Keqiang

End-to-end models are emerging as the mainstream in autonomous driving perception. However, the inability to meticulously deconstruct their internal mechanisms results in diminished development efficacy and impedes the establishment of trust. Pioneer

Externí odkaz: http://arxiv.org/abs/2409.11969

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Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving

Autor: Dai, Yuqi, Sun, Jian, Li, Shengbo Eben, Xu, Qing, Wang, Jianqiang, He, Lei, Li, Keqiang

Perception is essential for autonomous driving system. Recent approaches based on Bird's-eye-view (BEV) and deep learning have made significant progress. However, there exists challenging issues including lengthy development cycles, poor reusability,

Externí odkaz: http://arxiv.org/abs/2407.12491

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OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation

Autor: Sun, Jian, Dai, Yuqi, Vong, Chi-Man, Xu, Qing, Li, Shengbo Eben, Wang, Jianqiang, He, Lei, Li, Keqiang

Bird's-eye-view (BEV) semantic segmentation is becoming crucial in autonomous driving systems. It realizes ego-vehicle surrounding environment perception by projecting 2D multi-view images into 3D world space. Recently, BEV segmentation has made nota

Externí odkaz: http://arxiv.org/abs/2407.13137

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