A Self-Trajectory Prediction Approach for Autonomous Vehicles Using Distributed Decouple LSTM

Vehicle trajectory prediction plays a crucial role in ensuring the driving safety of autonomous vehicles in complex traffic scenes. To accurately predict the trajectory of autonomous vehicles, in this article, we propose a distributed decouple long short-term memory (LSTM) self-trajectory prediction method for autonomous driving. The proposed new recurrent network includes a decouple-LSTM unit and corresponding distributed network architecture. To characterize the closed-loop dynamics of autonomous vehicles, a decouple gate and a control gate are proposed to build the decouple-LSTM unit. The data are processed in different ways according to whether the data participates in the recurrent. The decouple gate filters the data participating in the recurrent, while the control gate handles the data outside the recurrent. By leveraging the decouple-LSTM unit, a distributed network architecture is established, which corresponds with the general vehicle motion control architecture, which effectively models the vehicle motion processes. The proposed method is trained using an actual vehicle dataset and validated through vehicle experiments. The prediction horizon ranges from 0.5 to 3 s. When the prediction horizon is set to 3 s, compared with the LSTM method, the mean square error of the proposed method decreases by 98.0%. Results show that the proposed method significantly improves vehicle trajectory prediction accuracy.