An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network

Vehicle trajectory prediction is essential for the operation safety and control efficiency of automated driving. Prevailing studies predict car following and lane change processes in a separate manner, ignoring the dependencies of these two behaviors. To remedy this issue, this paper proposes an integrated deep learning-based two-dimension trajectory prediction model that can predict combined behaviors. Specifically, we designed a switch neural network structure based on the attention mechanism, bi-directional long-short term memory (BiLSTM) and Temporal convolution neural network (TCN) to mimic and predict the joint behaviors. Experiments are conducted based on the Next Generation Simulation (NGSIM) dataset to validate the effectiveness of our proposed model. As results indicate, our proposed model outperforms the state-of-art trajectory prediction models and can provide accurate short-term and long-term predictions. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a deep learning-based trajectory prediction model that can predict the combined behaviors of car following and lane change processes. Experimental results demonstrate that the proposed model outperforms other models in both short-term and long-term predictions.