Pedestrian Trajectory Intention Prediction in Autonomous Driving Scenarios Based on Spatio-temporal Attention Mechanism

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of pedestrians that pose a risk to it. Due to the uncertainty of human intentions, accurately predicting pedestrian trajectory intentions is a great challenge. This paper proposes a novel spatio-temporal attention framework for pedestrian trajectory prediction in autonomous driving scenarios. The framework consists of three key components: a spatio-temporal feature extraction module, a multi-head attention mechanism for trajectory encoding, and an intention recognition module. The spatio-temporal feature extraction module captures both local motion patterns and global interaction contexts through a hierarchical architecture. The multi-head attention mechanism processes trajectory information in parallel streams, enabling comprehensive feature learning across different temporal scales. The intention recognition module explicitly models the relationship between trajectory patterns and pedestrian intentions, improving prediction accuracy and interpretability. Extensive experiments on the ETH-UCY and Stanford Drone datasets demonstrate the effectiveness of our approach. The proposed method achieves significant improvements over state-of-the-art methods, with a 12.8% reduction in average displacement error and 93% intention recognition accuracy. The framework maintains real-time performance capabilities, making it suitable for practical autonomous driving applications.