Interactive Vehicle Trajectory Prediction Based on Parameterized Transfer Learning Using Encoder–Decoder Network

Vehicle trajectory prediction is important for automated vehicles to understand driving scenarios. This paper proposes an encoder-decoder network-based parameterized transfer learning (EDN-PTL) model to predict vehicle trajectory. To improve trajectory prediction accuracy, the motion interaction between the target vehicle and the surrounding vehicles is considered, and a multidimensional spatiotemporal input expansion (MSIA) strategy is proposed to extend the feature dimensions. Additionally, global and local scale features, as well as long and short horizon features, are extracted and used for interactive vehicle trajectory prediction by a CNN and LSTM-based encoder-decoder network (CNN-LSTM-EDN). Moreover, the features extracted by CNN-LSTM-EDN are integrated using a stacked convolutional social pooling network (SCSPN). To enhance the environmental adaptability of the trajectory prediction model, a PTL strategy is proposed to enable transfer learning capabilities of EDN-PTL. Based on the PTL strategy, trajectory prediction accuracy is maintained even when applied to untrained environments. The proposed EDN-PTL model is validated on three types of publicly available naturalistic datasets and compared with several baselines and state-of-the-art (SOTA) methods. The validation results demonstrate that the proposed EDN-PTL achieves better prediction accuracy, robustness, and environmental adaptability compared to the baselines and SOTA methods.