Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction
This work addresses a limitation in trajectory prediction for autonomous driving and robotics, though it is incremental by building on existing methods for dynamic feature extraction.
The paper tackles the problem of fixed-length output predictions in trajectory prediction by introducing the FlexiSteps Network (FSN), which dynamically adjusts prediction time steps based on context, resulting in improved accuracy and efficiency as demonstrated on Argoverse and INTERACTION datasets.
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.