# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 1 Introduction  
Intelligent vehicles, equipped with advanced sensing systems, are transforming the way we perceive and navigate driving environments. These vehicles can gather extensive amounts of contextual information, surpassing the capabilities of human drivers. This advanced perceptual capability serves as a critical foundation for comprehending the driving environment and making accurate decisions in autonomous vehicle operations. Based on these sensor inputs, autonomous vehicles must infer the intentions of surroundingvehicles and accurately predict their future trajectories [1,2]. Such highly accurate trajectory predictions are crucial not only for anticipating upcoming driving conditions but also for informing the subsequent tactical decisions of autonomous vehicles [3]. In the context of complex urban road networks, decision-making in intelligent vehicles is ultimately reflected in their trajectory and driving behavior. Predicting a vehicle's future trajectory is therefore a pivotal factor in ensuring safety in dynamic traffic conditions [4]. Furthermore, trajectory prediction is indispensable for modeling traffic states, facilitating decision-making, planning, and anticipating hazards. Accurately forecasting the movements of nearby vehicles is essential for the effective operation of intelligent vehicles. Yet, achieving consistent precision in trajectory predictions, particularly in challenging environments, remains a notable challenge [5]. Simultaneously, the diversity in driving behaviors and the inherent complexity of urban traffic also pose considerable challenges to achieving reliable and accurate predictions [6]. Traditional trajectory prediction models often overemphasize historical trajectory data, neglecting the fact that their influence may diminish over time. It is essential to evaluate the relevance of historical features across varying temporal horizons [7]. Moreover, many models address temporal and spatial attention independently, thereby overlooking their interdependencies. Beyond spatial feasibility and target positions, a driver's style plays a significant role in influencing trajectory modifications. To enhance accuracy and stability, these challenges must be systematically addressed [8,9]. Recent studies [10] have categorized vehicle trajectory prediction approaches into four primary methodologies: physical models, machine learning, reinforcement learning, and deep learning. Among these, traditional methods, such as dynamic models, kinematic models [11], Kalman filtering [12], and Monte Carlo methods [13], utilize features like vehicle speed, acceleration, and turning rate. Although these approaches are computationally efficient, they often struggle to maintain accuracy in complex traffic scenarios. Machine learning approaches have introduced new paradigms for trajectory prediction. For example, [PERSON] et al. [14] integrated model-driven interaction perception with supervised learning, while [PERSON] et al. [15] employed SVM and GMM to analyze multi-participant interactions. However, these models often require deterministic maneuver characteristics, limiting them to simpler, short-term scenarios. Reinforcement learning (RL) has emerged as a promising framework for managing the complexity and high dimensionality of driving environments. Approaches such as HRL [16] and GAIL [17] have demonstrated efficacy in autonomous driving policy learning. However, RL's computational intensity and lengthy training times hinder its feasibility for real-time applications. Deep learning has gained significant prominence, marked by advancements in sequence models, graph neural networks (GNNs), and generative models. Sequence networks extract feature sequences from trajectory data, with models such as MFP [18] and mmTransformer [19] integrating environmental context for enhanced predictions. GNNs, such as TrGNN [20] and hierarchical GNN models [21], capture interdependencies among traffic participants.