Map-enhanced generative adversarial trajectory prediction method for automated vehicles

Trajectory prediction in dynamic and highly interactive scenarios is a critical method for achieving advanced autonomous driving. Maximizing the guidance and constraints pro-vided by high-definition (HD) maps can help improve prediction performance across the board. In this paper, we propose a map-enhanced generative adversarial network (ME -GAN) for vehicle trajectory prediction. The vehicle motion features, map constraints, traffic flow density, and vehicle interactions are comprehensively considered in the generator, and a graph query mechanism is proposed to realize the reuse of the global map. In the dis-criminator, in addition to considering the authenticity of a generated trajectory and whether it is consistent with the historical trajectory, additional map information is intro-duced to establish a matching model between the generated trajectory and the current map. Experiments based on the Argoverse and nuScenes dataset are subsequently per -formed. The experimental results show that our prediction method outperforms state-of -the-art prediction systems, namely, TNT, PRIME and P2T. The strong coupling of the HD map significantly improves the reasonableness of the predicted trajectory.CO 2022 Elsevier Inc. All rights reserved.