Scipediacontent: Scipediacontent moved page Review 739373817341 to Li et al 2026e

2026-06-12T07:56:51Z

Scipediacontent moved page Review 739373817341 to Li et al 2026e

Scipediacontent: Scipediacontent moved page Draft content 971187075 to Review 739373817341

2026-06-12T07:50:06Z

Scipediacontent moved page Draft content 971187075 to Review 739373817341

Scipediacontent: Created page with " == Abstract ==
Accurate prediction of future vehicle trajectories is essential for ensuring safety and reliable decision-making in autonomous driving systems. However, ex..."

2026-06-12T07:50:04Z

Created page with " == Abstract == <p>Accurate prediction of future vehicle trajectories is essential for ensuring safety and reliable decision-making in autonomous driving systems. However, ex..."

New page

== Abstract ==

<p>Accurate prediction of future vehicle trajectories is essential for ensuring safety and reliable decision-making in autonomous driving systems. However, existing deep learning-based approaches exhibit several limitations. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) struggle to effectively model long-term temporal dependencies and complex agent interactions, while Transformer-based architectures often suffer from high computational complexity and limited efficiency. To overcome these challenges, this paper proposes an efficient Mamba-based feature extraction framework for jointly encoding vehicle trajectories and map information. By leveraging state-space modeling and a selective scanning mechanism, the proposed approach effectively captures longrange dependencies and enhances the representation of complex traffic behaviors. Specifically, raw scene data are first normalized and embedded into a unified feature space. A Mamba Encoder is then employed to extract high-level features from historical vehicle trajectories and map elements. Subsequently, Vehicle-Vehicle and Vehicle-Map interaction modules are introduced to explicitly model dynamic interactions among traffic participants and between vehicles and the surrounding map. The resulting high-dimensional features are further fused using an additional Mamba Encoder, while a Global Interaction Module is designed to capture scenelevel dependencies. Finally, a Gated Recurrent Unit (GRU) decoder generates multi-modal future trajectory predictions. Experimental results on the Argoverse 1 dataset demonstrate that the proposed method achieves superior performance in terms of minADE, minFDE, and minMR, while maintaining high computational efficiency.OPEN ACCESS Received: 28/01/2026 Accepted: 16/04/2026</p>

== Document ==
<pdf>Media:Draft_content_971187075-6421-document.pdf</pdf>

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Li et al 2026e - Revision history

Scipediacontent: Scipediacontent moved page Review 739373817341 to Li et al 2026e

Scipediacontent: Scipediacontent moved page Draft content 971187075 to Review 739373817341

Scipediacontent: Created page with " == Abstract == Accurate prediction of future vehicle trajectories is essential for ensuring safety and reliable decision-making in autonomous driving systems. However, ex..."

Scipediacontent: Created page with " == Abstract ==
Accurate prediction of future vehicle trajectories is essential for ensuring safety and reliable decision-making in autonomous driving systems. However, ex..."