An Enhanced Vehicle Trajectory Prediction Model Leveraging LSTM and Social-Attention Mechanisms

Accurate trajectory prediction for multiple vehicles in complex social interaction environments is essential for ensuring the safety of autonomous vehicles and improving the quality of their planning and control. The social interactions between vehicles significantly influence their future trajectories. However, traditional trajectory prediction models based on Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) often overlook or simplify these interactions. Although these models may exhibit high performance in short-term predictions, they fail to achieve high prediction accuracy in scenarios with long-term dynamic interactions. To address this limitation, we propose a Social-Attention Long Short-Term Memory (LSTM) model which predicts the future trajectories of neighboring vehicles and achieves increased accuracy. Our proposed model employs a Social-Pooling layer to effectively capture cooperative behaviors and mutual influences between vehicles. Additionally, we incorporate a self-attention mechanism to weight the inputs and outputs of the Social-Pooling layer, which is significant for assessing the influence between vehicles in different positions. This combination allows our model to take into consideration both the dependencies within the sequence and the social relationships between vehicles, providing a more comprehensive scene understanding. The efficacy of our model is tested on two real-world freeway trajectory datasets, namely NGSIM and HighD. Our model surpasses various baseline methods, exhibiting exceptional accuracy in both prediction and tracking.