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A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction
Accurate and robust trajectory prediction of neighboring agents is critical
for autonomous vehicles traversing in complex scenes. Most methods proposed in
recent years are deep learning-based due to their strength in encoding complex
interactions. However, unplausible predictions are often generated since they
rely heavily on past observations and cannot effectively capture the transient
and contingency interactions from sparse samples. In this paper, we propose a
hierarchical hybrid framework of deep learning (DL) and reinforcement learning
(RL) for multi-agent trajectory prediction, to cope with the challenge of
predicting motions shaped by multi-scale interactions. In the DL stage, the
traffic scene is divided into multiple intermediate-scale heterogenous graphs
based on which Transformer-style GNNs are adopted to encode heterogenous
interactions at intermediate and global levels. In the RL stage, we divide the
traffic scene into local sub-scenes utilizing the key future points predicted
in the DL stage. To emulate the motion planning procedure so as to produce
trajectory predictions, a Transformer-based Proximal Policy Optimization (PPO)
incorporated with a vehicle kinematics model is devised to plan motions under
the dominant influence of microscopic interactions. A multi-objective reward is
designed to balance between agent-centric accuracy and scene-wise
compatibility. Experimental results show that our proposal matches the
state-of-the-arts on the Argoverse forecasting benchmark. It's also revealed by
the visualized results that the hierarchical learning framework captures the
multi-scale interactions and improves the feasibility and compliance of the
predicted trajectories