Adaptive output steps: FlexiSteps network for dynamic trajectory prediction

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 a pre-trained Adaptive Prediction Module (APM) to intelligently determine optimal prediction horizons and a Dynamic Decoder (DD) module that enables flexible output generation across different time steps. Additionally, to balance prediction horizon and accuracy, we design a scoring mechanism that leverages Fréchet distance to evaluate geometric similarity between predicted and ground truth trajectories while considering prediction length, enabling principled trade-offs between prediction horizon and accuracy. Our plug-and-play design allows seamless integration with existing trajectory prediction models. Extensive experiments on benchmark datasets including Argoverse and INTERACTION demonstrate that FSN achieves superior prediction accuracy and contextual adaptability compared to traditional fixed-step approaches.