Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

Abstract

Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous vehicles and social robots) to achieve safe and high-quality planning when they navigate in highly interactive and crowded scenarios. Due to the existence of frequent interactions and uncertainty in the scene evolution, it is desired for the prediction system to enable relational reasoning on different entities and provide a distribution of future trajectories for each agent. In this paper, we propose a generic generative neural system (called Social-WaGDAT) for multi-agent trajectory prediction, which makes a step forward to explicit interaction modeling by incorporating relational inductive biases with a dynamic graph representation and leverages both trajectory and scene context information. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction which not only ensures physical feasibility but also enhances model performance. The proposed system is evaluated on three public benchmark datasets for trajectory prediction, where the agents cover pedestrians, cyclists and on-road vehicles. The experimental results demonstrate that our model achieves better performance than various baseline approaches in terms of prediction accuracy.

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• EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
• Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
• SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
• Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Prediction
• Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
• LTN: Long-Term Network for Long-Term Motion Prediction
• Prediction of Pedestrian Spatiotemporal Risk Levels for Intelligent Vehicles: A Data-driven Approach