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PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information | | Thomas Kurbiel
; Akash Sachdeva
; Kun Zhao
; Markus Buehren
; | | Date: |
2 Oct 2020 | | Abstract: | The ability to predict multiple possible future positions of the ego-vehicle
given the surrounding context while also estimating their probabilities is key
to safe autonomous driving. Most of the current state-of-the-art Deep Learning
approaches are trained on trajectory data to achieve this task. However
trajectory data captured by sensor systems is highly imbalanced, since by far
most of the trajectories follow straight lines with an approximately constant
velocity. This poses a huge challenge for the task of predicting future
positions, which is inherently a regression problem. Current state-of-the-art
approaches alleviate this problem only by major preprocessing of the training
data, e.g. resampling, clustering into anchors etc. In this paper we propose an
approach which reformulates the prediction problem as a classification task,
allowing for powerful tools, e.g. focal loss, to combat the imbalance. To this
end we design a generative probabilistic model consisting of a deep neural
network with a Mixture of Gaussian head. A smart choice of the latent variable
allows for the reformulation of the log-likelihood function as a combination of
a classification problem and a much simplified regression problem. The output
of our model is an estimate of the probability density function of future
positions, hence allowing for prediction of multiple possible positions while
also estimating their probabilities. The proposed approach can easily
incorporate context information and does not require any preprocessing of the
data. | | Source: | arXiv, 2010.00802 | | Services: | Forum | Review | PDF | Favorites | |
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