X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Prediction

TL;DR

This paper introduces X-TRACK, a physics-aware xLSTM-based framework for vehicle trajectory prediction that incorporates kinematic constraints to produce realistic trajectories, outperforming existing models on standard datasets.

Contribution

The paper presents a novel xLSTM-based trajectory prediction model that explicitly integrates vehicle kinematic constraints for improved realism and accuracy.

Findings

X-TRACK outperforms state-of-the-art baselines on highD and NGSIM datasets.

Incorporating physical constraints improves trajectory realism.

The physics-aware xLSTM effectively models vehicle motion dynamics.

Abstract

Recent advancements in Recurrent Neural Network (RNN) architectures, particularly the Extended Long Short Term Memory (xLSTM), have addressed the limitations of traditional Long Short Term Memory (LSTM) networks by introducing exponential gating and enhanced memory structures. These improvements make xLSTM suitable for time-series prediction tasks as they exhibit the ability to model long-term temporal dependencies better than LSTMs. Despite their potential, these xLSTM-based models remain largely unexplored in the context of vehicle trajectory prediction. Therefore, this paper introduces a novel xLSTM-based vehicle trajectory prediction framework, X-TRAJ, and its physics-aware variant, X-TRACK (eXtended LSTM for TRAjectory prediction Constraint by Kinematics), which explicitly integrates vehicle motion kinematics into the model learning process. By introducing physical constraints, the proposed model generates realistic and feasible trajectories. A comprehensive evaluation on the highD and NGSIM datasets demonstrates that X-TRACK outperforms state-of-the-art baselines.