Print Email Facebook Twitter Unravelling uncertainty in trajectory prediction using a non-parametric approach Title Unravelling uncertainty in trajectory prediction using a non-parametric approach Author Li, G. (TU Delft Transport and Planning) Li, Zirui (Beijing Institute of Technology) Knoop, V.L. (TU Delft Transport and Planning) van Lint, J.W.C. (TU Delft Transport and Planning) Date 2024 Abstract Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver's near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent's future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected. Subject Causal inferenceMicroscopic traffic modellingTrajectory predictionUncertainty quantification To reference this document use: http://resolver.tudelft.nl/uuid:1c4e964e-2681-4da0-9a08-4ee96759312c DOI https://doi.org/10.1016/j.trc.2024.104659 ISSN 0968-090X Source Transportation Research. Part C: Emerging Technologies, 163 Part of collection Institutional Repository Document type journal article Rights © 2024 G. Li, Zirui Li, V.L. Knoop, J.W.C. van Lint Files PDF 1-s2.0-S0968090X24001803-main.pdf 2.68 MB Close viewer /islandora/object/uuid:1c4e964e-2681-4da0-9a08-4ee96759312c/datastream/OBJ/view