G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction
CoRR(2024)
摘要
Predicting future trajectories of traffic agents accurately holds substantial
importance in various applications such as autonomous driving. Previous methods
commonly infer all future steps of an agent either recursively or
simultaneously. However, the recursive strategy suffers from the accumulated
error, while the simultaneous strategy overlooks the constraints among future
steps, resulting in kinematically infeasible predictions. To address these
issues, in this paper, we propose G2LTraj, a plug-and-play global-to-local
generation approach for trajectory prediction. Specifically, we generate a
series of global key steps that uniformly cover the entire future time range.
Subsequently, the local intermediate steps between the adjacent key steps are
recursively filled in. In this way, we prevent the accumulated error from
propagating beyond the adjacent key steps. Moreover, to boost the kinematical
feasibility, we not only introduce the spatial constraints among key steps but
also strengthen the temporal constraints among the intermediate steps. Finally,
to ensure the optimal granularity of key steps, we design a selectable
granularity strategy that caters to each predicted trajectory. Our G2LTraj
significantly improves the performance of seven existing trajectory predictors
across the ETH, UCY and nuScenes datasets. Experimental results demonstrate its
effectiveness. Code will be available at https://github.com/Zhanwei-Z/G2LTraj.
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