Distributed Multi-Attention Generative Adversarial Network for Surrounding Vehicles Trajectories Prediction Based On Comprehensive Social Repulsion

Abstract

Trajectories prediction for vehicles is a key task in autonomous driving. This task is complicated by the presence of social interactions between vehicles and their physical constraints with the scene. From dominant and concealed characteristics of the driving scenarios, this paper proposes a distributed multi-attention generative adversarial network named DTMA-GAN. Firstly, temporal and spatial attention heads are used in the framework of DTMA-GAN to extract scene information, driving features of self-vehicles and social interaction features among vehicles. These features help the model learn the position in the large scene and capture the most salient parts of the image associated with the path. Secondly, a subset of the Euclidean space is modeled using the lane environment and a collision-free set of states is constructed to generate a safe driving area via the environment occupancy points set. Finally, the multiple trajectories with confidence generated by GAN are combined with this candidate driving area, and an accurate, reasonable, and long-term vehicle trajectory is selected using non-maximum suppression. And we use publicly available NGSIM and Argoverse datasets for model evaluation. Experimental results show that compared with CS-LSTM, GRIP and other excellent algorithms, this algorithm can effectively reduce the Root Mean Square Error for trajectory prediction in dynamic environments. And ablation experiments show that all the module components are effective.

Keywords

• Ablation experiments
• automated driving
• GAN
• LSTM
• vehicle trajectory prediction