English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A multitude of publicly-available driving datasets and data platforms havebeen raised for autonomous vehicles (AV). However, the heterogeneities ofdatabases in size, structure and driving context make existing datasetspractically ineffective due to a lack of uniform frameworks and searchableindexes. In order to overcome these limitations on existing public datasets,this paper proposes a data unification framework based on traffic primitiveswith ability to automatically unify and label heterogeneous traffic data. Thisis achieved by two steps: 1) Carefully arrange raw multidimensional time seriesdriving data into a relational database and then 2) automatically extractlabeled and indexed traffic primitives from traffic data through a Bayesiannonparametric learning method. Finally, we evaluate the effectiveness of ourdeveloped framework using the collected real vehicle data.

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives