A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
This addresses the lack of uniform frameworks for autonomous vehicle datasets, but it is incremental as it builds on existing data platforms.
The paper tackles the problem of heterogeneous driving datasets for autonomous vehicles by proposing a data unification framework based on traffic primitives, which automatically unifies and labels data using a relational database and Bayesian nonparametric learning, and evaluates it with real vehicle data.
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.