A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
By Jiacheng Zhu, Wenshuo Wang, Ding Zhao. 2018
6 citations recorded by the source corpus.
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.
Subfields: Building and Construction, Signal Processing
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In Signal Processing
Citations in the corpus
- Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, methodology): “approach using time series data available from the GPU and IMU which were later used to extract driving primitives using unsupervised learning methods such as clustering or Bayesian optimisation [102].”
- Survey on Scenario-Based Safety Assessment of Automated Vehicles (2020, background): “[37] also show an overview of data sets and try to unify them.”