Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction

2025-08-25

AI Summary

Traditional trajectory prediction models are constrained by fixed output horizons, limiting their adaptability to dynamic real-world scenarios. To address this, we propose FlexiSteps—a novel framework introducing the first adaptive timestep selection module for trajectory forecasting. Our method jointly optimizes geometric trajectory similarity (measured via Fréchet distance) and timestep plausibility (via a weighted scoring mechanism) to enable context-aware, variable-length prediction horizons. FlexiSteps comprises a pre-trained adaptive prediction head, a dynamic decoder, and an end-to-end differentiable timestep selection mechanism. Extensive experiments on Argoverse and INTERACTION demonstrate that FlexiSteps significantly outperforms baseline models in both average displacement error (ADE) and final displacement error (FDE), while improving computational efficiency and scene adaptability. By enabling flexible, input-dependent horizon lengths, FlexiSteps establishes a scalable, multimodal paradigm for long-horizon trajectory prediction.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Dynamically adjusts prediction output time steps
• Ensures optimal prediction accuracy and efficiency
• Balances prediction time steps and accuracy

Innovation

• Dynamic adjustment of prediction output time steps
• Pre-trained Adaptive Prediction Module for evaluation
• Scoring mechanism balancing step length and accuracy