Peidong Li

2 accepted papers

2026

Rethinking the Spatio-Temporal Alignment of End-to-End 3D Perception

Xiaoyu Li, Peidong Li, Xian Wu, Long Shi, Dedong Liu, Yitao Wu +4

AAAI 2026technical

Spatio-temporal alignment is crucial for temporal modeling of end-to-end (E2E) perception in autonomous driving (AD), providing valuable structural and textural prior information. Existing methods typically rely on the attention mechanism to align objects across frames, simplifying the motion model

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2025

Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving

Peidong Li, Dixiao Cui

ICLR 2025poster

End-to-End Autonomous Driving (E2EAD) methods typically rely on supervised perception tasks to extract explicit scene information (e.g., objects, maps). This reliance necessitates expensive annotations and constrains deployment and data scalability in real-time applications. In this paper, we introd…

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