Transformer Based Trajectory Prediction

Transformer-based trajectory prediction aims to forecast the future movements of objects, particularly pedestrians and vehicles, leveraging the power of deep learning to model complex spatio-temporal dependencies. Current research emphasizes improving prediction accuracy across varying observation lengths and incorporating contextual information like social interactions and environmental constraints, often using transformer architectures enhanced with techniques such as attention mechanisms and graph neural networks. This field is crucial for advancing autonomous systems, improving traffic safety and management, and enabling more efficient robotic manipulation, with ongoing work focused on optimizing model efficiency and generalizability across diverse real-world scenarios.