A multitude of publicly-available driving datasets and data platforms have
been raised for autonomous vehicles (AV). However, the heterogeneities of
databases in size, structure and driving context make existing datasets
practically ineffective due to a lack of uniform frameworks and searchable
indexes. In order to overcome these limitations on existing public datasets,
this paper proposes a data unification framework based on traffic primitives
with ability to automatically unify and label heterogeneous traffic data. This
is achieved by two steps: 1) Carefully arrange raw multidimensional time series
driving data into a relational database and then 2) automatically extract
labeled and indexed traffic primitives from traffic data through a Bayesian
nonparametric learning method. Finally, we evaluate the effectiveness of our
developed framework using the collected real vehicle data.Comment: 6 pages, 7 figures, 1 table, ITSC 201