MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing

Autonomous driving related researches require the analysis and usage of massive amounts of driving scenario data. Compared to raw data collected by sensors, scenario data provide a preliminary abstraction of driving tasks and processes, explicitly integrate information about the road environment and the dynamic and static attributes of traffic participants, making it easier to conduct task understanding and decision making. However, many existing driving scenario datasets have the following two problems. First, it is not clear which data fields need to be recorded for driving scenarios. The data storage formats and organization standards are inconsistent. Second, the datasets cannot establish driving scenario indexing effectively. Existing datasets are sparsely annotated and difficult to index, which is detrimental to data sampling and extraction for machine learning process, thus hindering efficient fusion and reuse. In this paper, the authors propose MetaScenario, a framework for driving scenario data. The authors describe driving scenarios and design the centralized and unified data framework for the storage, processing, and indexing of scenario data based on relational database. The concept of atom scenario is proposed and characterized using semantic graphs. The authors also annotate and classify behaviors and interactions of traffic participants in atom scenarios by extracting the spatiotemporal evolution of semantic information. The annotation facilitates the indexing and extraction of data. The scenario datasets are further evaluated via the data distribution and annotation statistics. MetaScenario can provide researchers with convenient tools for scenario data extraction and important analytical references.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2022.3215503
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Chang, Cheng
  • Cao, Dongpu
  • Chen, Long
  • Su, Kui
  • Su, Kuifeng
  • Su, Yuelong
  • Wang, Fei-Yue
  • Wang, Jingcheng
  • Wang, Ping
  • Wei, Junqing
  • Wu, Gansha
  • Wu, Xiangbin
  • Xu, Haotian
  • Zheng, Nanning
  • Li, Lin
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1156-1175
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Data mining; Data storage; Driving; Forecasting; Indexing; Relational databases; Roads; Traffic characteristics; Trajectory
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01881577
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 30 2023 4:51PM