Parameter-Efficient Vehicle Trajectory Prediction Based on Attention-Enhanced Liquid Structural Neural Model

Abstract

Due to advances in sensor techniques and deep learning, autonomous vehicular technologies have become more reliable and practical. Trajectory prediction is a critical task to anticipate the future positions of surrounding vehicles. However, existing algorithms, such as LSTM-based and attention-based models, face challenges of high computational complexity, large parameter sizes, and limited ability to efficiently capture both temporal dependencies and spatial interactions in dynamic traffic scenarios. In this paper, we propose a parameter-efficient trajectory prediction model that integrates Liquid Time-Constant (LTC) networks with attention mechanisms, termed the Attn-LTC model. The key contributions of our work are threefold. First, we introduce a temporal attention-enhanced LTC encoder that effectively captures both long-term temporal dependencies and dynamic behaviors from historical trajectory data. Second, we incorporate a spatial attention-enhanced LTC decoder, which emphasizes the influence of neighboring vehicles and spatial interactions, thereby improving prediction accuracy. Third, we demonstrate the computational efficiency of the Attn-LTC model, which achieves high predictive accuracy with significantly fewer parameters compared to LSTM-based and Transformer-based counterparts. Extensive experiments conducted on the NGSIM dataset demonstrate the advantages of our proposed Attn-LTC model. Notably, it reduces computational complexity and model size while maintaining superior accuracy, making it well suited for deployment in resource-constrained systems. The results highlight the effectiveness of the Attn-LTC model in balancing precision and efficiency, paving the way for its application in real-time autonomous driving systems.

Keywords

• trajectory prediction
• ordinary differential equation
• attention mechanism
• autonomous driving
• computational efficiency