Vehicle Trajectory Prediction Based on Attention Mechanism and GAN

To address the problem that the Social Generative Adversarial Network (SGAN) cannot fully extract the hidden state of vehicle movement, and does not get enough interactive information between vehicles, a vehicle trajectory prediction model Attentive Generative Adversarial Network (AGAN) based on the attention mechanism and the generative adversarial network is proposed. Among them, the historical attention mechanism calculates the focus of the vehicle in the historical hidden state, and the social attention mechanism calculates the weight of the influence of surrounding vehicles on the target vehicle. Combining historical and social attention mechanisms can obtain vehicle movement information that includes both time and space influencing factors. With the help of the generative adversarial network for global joint training, it is possible to generate a future trajectory that conforms to physical constraints and social norms. Experiments show that compared with SGAN model, AGAN in the ADE model and FDE index decreased by 4.4% and 3.8%.