Intention Recognition and Trajectory Prediction for Vehicles Using LSTM Network

Autonomous vehicles often need to predict the trajectories of surrounding vehicles for planning and decision making. In this paper, a model for intention recognition and trajectory prediction based on long short-term memory (LSTM) network is proposed. The proposed model comprises an intention recognition module and a trajectory output module. The intention recognition module was employed for identifying the driving intention. The Softmax function was incorporated in the intention recognition module for calculating the probabilities of left lane change, lane-keeping, and right lane change. An encoder-decoder structure and a mixture density network (MDN) layer were included in the trajectory output module. The encoder converted the past trajectory information into the context vector. Subsequently, the decoder combined the context vector and the intention recognition information for predicting future trajectories. The MDN layer was employed for representing the future position of a vehicle with its probability distribution rather than with a particular trajectory, which improved the reliability of prediction results and the robustness of the proposed model. Additionally, the compositions of a predicted vehicle and its surroundings were both taken into account, which aided the proposed model in analyzing the interactions among vehicles. Hence, the proposed model can dynamically predict vehicle trajectories according to variations in traffic conditions. The NGSIM data set based on the information of actual road conditions was employed for training, validating, and testing the proposed model. Experimental results indicate that the proposed method based on LSTM network has several advantages over conventional model-based methods with respect to trajectory prediction, especially in a long prediction horizon. Interactive information can ensure that the intention recognition module has high anticipative ability and accuracy. Furthermore, trajectory prediction based on intention recognition can significantly reduce the root-mean-square errors in predicted trajectories with respect to the ground truth, thereby leading to significant improvement in trajectory prediction accuracy. © 2019, Editorial Department of China Journal of Highway and Transport. All right reserved.