Safety-Aware Vehicle Trajectory Prediction With Spatiotemporal Attentional GAN in Hybrid Transportation System

Abstract

During the transition from human-driven vehicles to a fully connected automated vehicle (CAV) traffic environment, the hybrid transportation system in which human-driven vehicles (HDVs) and CAVs coexist will face challenges owing to the uncertainty of HDVs trajectories. Accurately predicting the trajectories of HDVs and conducting safety warnings for them is essential to improve the safety of the hybrid transportation system. In this paper, we propose a safety-aware vehicle trajectory prediction with spatio-temporal attentional GAN (SSTAttGAN) in the hybrid transportation system. Considering multiple driving characteristics, a driving pattern clustering mechanism based on spectral clustering is introduced to analyze vehicle behavior from the perspective of enhancing safety. In the GAN-based trajectory prediction model, a spatio-temporal attention mechanism is proposed as a module of the GAN generator, through which optimal weight distributions of various spatiotemporal features affecting trajectories can be obtained to improve prediction accuracy. The experimental evaluation on the real trajectory dataset demonstrates that our scheme outperforms state-of-the-art methods. Specifically, the Root Mean Square Error (RMSE) of our model on the Next Generation Simulation (NGSIM) dataset is 0.39 and 2.09 m in the predicted 1-s and 5-s time horizons, respectively. And the RMSE on the HighD dataset is 0.09 and 0.92 m in the predicted 1-s and 5-s time horizons, respectively.