动态调整预测步数，让轨迹预测更适应真实场景变化。
Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction
- 根据环境动态调整输出时间步，不再固定预测长度。
- 在Argoverse和INTERACTION数据集上显著提升预测精度与效率。
- 适合自动驾驶、机器人等需要灵活响应的实时系统。
准确的轨迹预测对自动驾驶、机器人及智能决策系统至关重要，但传统模型通常采用固定长度输出，难以适应动态现实场景。本文提出FlexiSteps Network（FSN），一种可根据上下文条件动态调整预测时间步的新框架。受观测长度差异和动态特征提取进展启发，FSN引入预训练的自适应预测模块（APM）以评估并动态调节输出步数，确保预测精度与效率最优。为实现即插即用，还设计了动态解码器（DD）。同时，通过评分机制平衡预测步数与准确性，结合弗雷谢距离评估预测轨迹与真实轨迹的几何相似性，并考虑预测步长。在Argoverse和INTERACTION等基准数据集上的大量实验验证了所提FSN框架的有效性与灵活性。
原文摘要 · Abstract (English)
Accurate trajectory prediction is vital for autonomous driving, robotics, and intelligent decision-making systems, yet traditional models typically rely on fixed-length output predictions, limiting their adaptability to dynamic real-world scenarios. In this paper, we introduce the FlexiSteps Network (FSN), a novel framework that dynamically adjusts prediction output time steps based on varying contextual conditions. Inspired by recent advancements addressing observation length discrepancies and dynamic feature extraction, FSN incorporates an pre-trained Adaptive Prediction Module (APM) to evaluate and adjust the output steps dynamically, ensuring optimal prediction accuracy and efficiency. To guarantee the plug-and-play of our FSN, we also design a Dynamic Decoder(DD). Additionally, to balance the prediction time steps and prediction accuracy, we design a scoring mechanism, which not only introduces the Fréchet distance to evaluate the geometric similarity between the predicted trajectories and the ground truth trajectories but the length of predicted steps is also considered. Extensive experiments conducted on benchmark datasets including Argoverse and INTERACTION demonstrate the effectiveness and flexibility of our proposed FSN framework.
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