Uncertainty estimation for Cross-dataset performance in Trajectory prediction

• URL: http://arxiv.org/abs/2205.07310v1
• Date: Sun, 15 May 2022 15:28:02 GMT
• Title: Uncertainty estimation for Cross-dataset performance in Trajectory prediction
• Authors: Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
• Abstract summary: We study the performance of a state-of-the-art trajectory prediction method across four different datasets. We highlight which datasets work best on others, and study how uncertainty estimation allows for a better transferable performance.
• Score: 1.9399281609371257
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While a lot of work has been done on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods across dataset. In this paper, we study the performance of a state-of-the-art trajectory prediction method across four different datasets (Argoverse, NuScenes, Interaction, Shifts). We first check how a similar method can be applied and trained on all these datasets with similar hyperparameters. Then we highlight which datasets work best on others, and study how uncertainty estimation allows for a better transferable performance; proposing a novel way to estimate uncertainty and to directly use it in prediction.

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