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

In a mixed traffic environment of human and autonomous driving, an autonomous vehicle must 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 spatiotemporal 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 intention recognition module explicitly models the relationship between trajectory Extensive experiments on the ETH-UCY and Stanford Drone datasets demonstrate the effectiveness of our approach. The proposed method significantly improves 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.