An Adversarial Learned Trajectory Predictor with Knowledge-Rich Latent Variables

Forecasting human trajectories is critical for different applications, such as autonomous driving and social robot. Recent works predict future trajectories by using a generative model, in which human motion is encoded with recurrent neural network. However, the latent variable needed in the generative model is always either a random Gaussian noise or encoded from a scene. In this work, we focus on generating the latent variable from the trajectory itself. Specifically, we propose a latent variable predictor, which can bridge the gap between latent variable distributions of observed and ground truth trajectories. We evaluate the proposed method on several benchmarking datasets. Results demonstrate that the proposed method outperforms state-of-the-art methods in average and final displacement errors. In addition, the ablation study indicates that the prediction performance will not dramatically decrease as sampling times decline during tests.