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

Many players in the automotive field support scenario-based assessment ofautomated vehicles (AVs), where individual traffic situations can be testedand, thus, facilitate concluding on the performance of AVs in differentsituations. Since a large number of different scenarios can occur in real-worldtraffic, the question is how to find a finite set of relevant scenarios.Scenarios extracted from large real-world datasets represent real-world trafficsince real driving data is used. Extracting scenarios, however, is challengingbecause (1) the scenarios to be tested should assess the AVs behave safely,which conflicts with the fact that the majority of the data contains scenariosthat are not interesting from a safety perspective, and (2) extensive dataprocessing is required, which hinders the utilization of large real-worlddatasets. In this work, we propose an approach for extracting scenarios fromreal-world driving data. The first step is data preprocessing to tackle theerrors and noise in real-world data by reconstructing the data. The second stepperforms data tagging to label actors' activities, their interactions with eachother and the environment. Finally, the scenarios are extracted by searchingfor combinations of tags. The proposed approach is evaluated using datasimulated with CARLA and applied to a part of a large real-world drivingdataset, i.e., the Waymo Open Motion Dataset (WOMD). The code and scenariosextracted from WOMD are open to the research community to facilitate theassessment of the automated driving functions in different scenarios.

Scenario Extraction from a Large Real-World Dataset for the Assessment of Automated Vehicles