A Hybrid Model Based on CNN-MTF-LSTM Architecture Integrated Attention Mechanism for Vehicle Trajectory Prediction
TL;DR
Comprehensive experiments on the NGSIM dataset validate the proposed model, demonstrating robust performance across structured highway driving scenarios and both the accuracy and computational efficiency of the proposed architecture.
Abstract
Accurate vehicle trajectory prediction is essential for autonomous driving systems. However, in dynamic traffic environments, existing methods often fail to fully capture complex spatiotemporal interactions, resulting in error accumulation and degraded stability over extended prediction horizons. To address these limitations, we propose a hybrid CNN-MTF-LSTM architecture with an integrated attention mechanism. The model comprises three core components: 1) a dual-branch encoder that separately processes temporal dependencies through LSTM and spatial interactions through CNN; 2) a Bahdanau attention module that dynamically selects the most relevant historical context at each decoding step; and 3) a multi-task fusion decoder, which is trained using a hybrid teacher-forcing strategy, to output kinematically consistent predictions of future positions, velocities, and accelerations. Comprehensive experiments on the NGSIM dataset validate the proposed model, demonstrating robust performance across structured highway driving scenarios. Comparative results against several baseline methods confirm both the accuracy and computational efficiency of the proposed architecture.