Freeway Trajectory Prediction via SpatiotemporalTransformers

For autonomous driving, accurate trajectory prediction is paramount, necessitating effective harnessing of spatiotemporal data. This study proposes an innovative Spatiotemporal Transformer-based model, enhancing trajectory prediction precision by leveraging a multi-head self-attention mechanism. This mechanism intricately captures both inter-vehicular interactions and temporal trajectory dependencies. The model, structured around an LSTM-based encoder-decoder framework, innovatively considers spatial interactions among observed and future trajectories during the decoding process. Evaluated on the NGSIM dataset, our model demonstrates superior predictive accuracy over existing methodologies, underscoring the efficacy of Spatiotemporal Transformers in complex dynamic environments.