Vehicle trajectory prediction based on the integration of long- and short-term spatiotemporal features and trajectory...

With the continuous advancement of autonomous driving technology, accurate vehicle trajectory prediction plays a crucial role in preventing potential traffic collisions. To address the limitations of existing methods-specifically the insufficient exploitation of historical trajectory information and the inaccurate modeling of multi-vehicle interactions-this study proposes a method that combines multi-scale feature extraction with trajectory optimization, enabling precise prediction of target vehicle trajectories.

Targeting urban roads and highway scenarios with naturalistic driving data, surrounding interactive vehicles are first identified using the Pearson correlation coefficient, and historical feature inputs are selected via the Maximal Information Coefficient (MIC). The proposed CTS-Informer model integrates CNN and TCN decoders to capture local and long-range spatiotemporal dependencies from historical trajectories. It further leverages Informer's sparse attention and self-attention distillation to model global motion trends. Finally, a trajectory correction and smoothing module refines the output for enhanced continuity and realism in the predicted target vehicle trajectory.

The proposed model was validated on the NGSIM dataset, demonstrating superior performance over existing baseline methods. For a 5-s prediction horizon, the model achieved a 25-48% reduction in ADE and a 12-42% reduction in RMSE. Component ablation experiments further showed that removing the CNN, TCN, or trajectory correction and smoothing module led to a performance drop of 12.8-32.6%, highlighting the contribution of each module to overall accuracy.

The study demonstrates that the CTS-Informer model delivers strong long-term prediction performance, with each component contributing effectively to overall accuracy. The results validate the effectiveness of multi-scale spatiotemporal feature fusion for extended-horizon prediction. Furthermore, the model provides reliable trajectory boundaries, providing robust reference input for autonomous driving decision-making systems.