Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles

Abstract

Trajectory prediction of surrounding vehicles is a critical task for connected and autonomous vehicles (CAVs), helping them to realize potential dangers in the traffic environment and make the most appropriate decisions. In a practical traffic environment, vehicles may affect each other, and the trajectories may have multi-modality and uncertainty, which makes accurate trajectory prediction a challenge. In this paper, we propose an interactive network model based on long short-term memory (LSTM) and a convolutional neural network (CNN) with a trajectory correction mechanism, using our newly proposed probability forcing method. The model learns the interactions between vehicles and corrects their trajectories during the prediction process. The output is a multimodal distribution of predicted trajectories. In the experimental evaluation of the US-101 and I-80 Next-Generation Simulation (NGSIM) real highway datasets, our proposed method outperforms other contrast methods.