A Novel Graph based Trajectory Predictor with Pseudo Oracle

Abstract

Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical problem in numerous applications, such as self-driving cars and socially aware robots. Challenges concentrate on capturing pedestrians' motion patterns and social interactions, as well as handling the future uncertainties. Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable. However, they do not integrate specific obstacle avoidance experience (OAE) that may improve prediction performance. For example, pedestrians' future trajectories are always influenced by others in front. Here we propose GTPPO (Graph-based Trajectory Predictor with Pseudo Oracle), an encoder-decoder-based method conditioned on pedestrians' future behaviors. Pedestrians' motion patterns are encoded with a long short-term memory unit, which introduces the temporal attention to highlight specific time steps. Their interactions are captured by a graph-based attention mechanism, which draws OAE into the data-driven learning process of graph attention. Future uncertainties are handled by generating multi-modal outputs with an informative latent variable. Such a variable is generated by a novel pseudo oracle predictor, which minimizes the knowledge gap between historical and ground-truth trajectories. Finally, the GTPPO is evaluated on ETH, UCY and Stanford Drone datasets, and the results demonstrate state-of-the-art performance. Besides, the qualitative evaluations show successful cases of handling sudden motion changes in the future. Such findings indicate that GTPPO can peek into the future.

17 apges, 8 figures

References in corpus (6)

• PiP: Planning-informed Trajectory Prediction for Autonomous Driving
• Multiple Futures Prediction
• Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
• Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model
• Stochastic trajectory prediction with social graph network
• SafeCritic: Collision-Aware Trajectory Prediction

Cited by in corpus (4)

• STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction
• Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multi-Agent Trajectory Prediction