Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction

• URL: http://arxiv.org/abs/2307.14788v1
• Date: Thu, 27 Jul 2023 11:29:57 GMT
• Title: Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction
• Authors: Tiago Rodrigues de Almeida and Oscar Martinez Mozos
• Abstract summary: We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. We also propose novel distance-based ranking proposals to assign probabilities to the generated trajectories. The overall system surpasses context-free deep generative models in human and road agents trajectory data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.

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