Abstract: The prediction of the motion of traffic participants is a crucial aspect for the research and
development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal
motion prediction, which requires the assignment of a probability score to each of the multiple
predicted motion hypotheses. However, there is a lack of ground truth for this probability score in
the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple
predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a
novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced
in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is
presented with the focus on urban intersections. The generation of probable future routes is (a) based
on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road
topology, and second, a clustering of similar routes that are driven in each cluster from the first step.
The estimation of the route probabilities is (b) based on a frequentist approach that considers how
traffic participants will move in the future given their motion history. PROMOTING is evaluated with
the publicly available Lyft database. The results show that PROMOTING is an appropriate approach
to estimate the probabilities of the future motion of traffic participants in urban intersections. In this
regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that
provides a probability score for probable future routes. Such a labeled dataset currently does not exist
and would be highly valuable for ML approaches with the task of multi-modal motion prediction.
The code is made open source.
