DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling

• URL: http://arxiv.org/abs/2503.15029v1
• Date: Wed, 19 Mar 2025 09:23:09 GMT
• Title: DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling
• Authors: Jianbo Zhao, Taiyu Ban, Zhihao Liu, Hangning Zhou, Xiyang Wang, Qibin Zhou, Hailong Qin, Mu Yang, Lei Liu, Bin Li,
• Abstract summary: Directional Rotary Position Embedding (DRoPE) is a novel adaptation of Rotary Position Embedding (RoPE) originally developed in natural language processing.<n>DRoPE overcomes limitations by introducing a uniform identity scalar into RoPE's 2D rotary transformation.<n> Empirical evaluations confirm DRoPE's good performance and significantly reduced space complexity.
• Score: 9.86959003425198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient modeling of agent interactions is essential for trajectory generation, the core of autonomous driving systems. Existing methods, scene-centric, agent-centric, and query-centric frameworks, each present distinct advantages and drawbacks, creating an impossible triangle among accuracy, computational time, and memory efficiency. To break this limitation, we propose Directional Rotary Position Embedding (DRoPE), a novel adaptation of Rotary Position Embedding (RoPE), originally developed in natural language processing. Unlike traditional relative position embedding (RPE), which introduces significant space complexity, RoPE efficiently encodes relative positions without explicitly increasing complexity but faces inherent limitations in handling angular information due to periodicity. DRoPE overcomes this limitation by introducing a uniform identity scalar into RoPE's 2D rotary transformation, aligning rotation angles with realistic agent headings to naturally encode relative angular information. We theoretically analyze DRoPE's correctness and efficiency, demonstrating its capability to simultaneously optimize trajectory generation accuracy, time complexity, and space complexity. Empirical evaluations compared with various state-of-the-art trajectory generation models, confirm DRoPE's good performance and significantly reduced space complexity, indicating both theoretical soundness and practical effectiveness. The video documentation is available at https://drope-traj.github.io/.

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