Multi-future Trajectory Prediction
9 papers with code • 5 benchmarks • 4 datasets
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution.
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications.
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment.
BiTraP estimates the goal (end-point) of trajectories and introduces a novel bi-directional decoder to improve longer-term trajectory prediction accuracy.
We propose to predict the future trajectories of observed agents (e. g., pedestrians or vehicles) by estimating and using their goals at multiple time scales.
Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation
AMD is a metric that quantifies how close the whole generated samples are to the ground truth.