ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations

1. 1Model the interactions between pedestrians using a graph-based or transformer-based framework
2. 2Introduce an adaptive relational transformer to capture temporal-aware relations
3. 3Evaluate the performance of the model on real-world pedestrian trajectory prediction tasks
4. 4Compare the results with existing state-of-the-art methods to demonstrate the effectiveness of the proposed approach

This research benefits machine learning engineers and researchers working on autonomous systems, robotics, and computer vision, as it provides a more efficient and effective approach to modeling human interactions

💡 The Adaptive Relational Transformer (ART) can effectively capture the diverse and time-varying characteristics of human interactions, improving the accuracy of pedestrian trajectory prediction

🚶‍♂️💻 ART: a novel transformer-based model for pedestrian trajectory prediction with temporal-aware relations