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Evaluation for entire trajectory forecasting pipeline on the KITTI and NuScenes datasets using the proposed evaluation procedure and metrics.
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Predicting the future is a crucial first step to effective control, since systems that can predict the future can select plans that lead to desired outcomes. In this work, we study the problem of future prediction at the level of 3D scenes, represented by point clouds captured by a LiDAR sensor, i.e., directly learning to forecast the evolution of...
Contexts in source publication
Context 1
... We summarize the performance for trajectory forecasting pipeline on the KITTI and NuScenes datasets in Table 2. The proposed new pipeline outperforms conventional pipeline using different stochastic forecasting modules with 1 sample. ...Context 2
... we only compare these three methods, we will integrate the ADE/FDE for all three methods at the recall values from 0.025, 0.050, · · · , to 0.425. For results in Table 2 of the main paper, the recall values that are used to compute the integral metrics are different across settings. ...Context 3
... We summarize the performance for trajectory forecasting pipeline on the KITTI and NuScenes datasets in Table 2. The proposed new pipeline outperforms conventional pipeline using different stochastic forecasting modules with 1 sample. ...Context 4
... we only compare these three methods, we will integrate the ADE/FDE for all three methods at the recall values from 0.025, 0.050, · · · , to 0.425. For results in Table 2 of the main paper, the recall values that are used to compute the integral metrics are different across settings. ...Similar publications
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