Efficacy of Early and Late Spatio-Temporal Fusion in LSTM Models for Vehicle Trajectory Prediction

Vehicle trajectory prediction is crucial for optimizing transportation systems, reducing traffic congestion, minimizing travel time, and enhancing overall safety. Accurately forecasting trajectories in complex traffic remains challenging due to both temporal vehicle dynamics and spatial interactions among neighboring vehicles. In this paper, we present a head-to-head comparison of two long short-term memory (LSTM) architectures that fuse spatio-temporal data at different stages: an early-fusion model that aggregates neighbor features at every time step, and a late-fusion model that encodes target and neighbor sequences separately and merges them only at the final prediction layer. A vanilla LSTM baseline that uses only the target vehicle's own history (no neighbor information) is also included to isolate the fusion benefit. All models are trained and evaluated on the NGSIM US-101 highway dataset using mean squared error (MSE), and mean absolute error (MAE) metrics. The results show that both fusion strategies yield modest error reductions compared to the baseline (vanilla LSTM MSE$\boldsymbol{=} \mathbf{1. 6 5} \boldsymbol{\rightarrow}$early-fusion MSE$\boldsymbol{=} \mathbf{0. 9 8 7 4}$, late-fusion MSE$=0.9910$), with only a slight advantage for early fusion. We provide qualitative analyses of lane-change versus lane-keep scenarios, discuss the limitations of our one-step prediction and simple neighbor pooling, and outline directions for future work, including attention mechanisms and graph-based models. The data and implementation are available at https://github.com/sukhjitsehra/FVTP.

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