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

文献类型：期刊论文

作者Chang, Cheng9; Cao, Dongpu10; Chen, Long11; Su, Kui12; Su, Kuifeng; Su, Yuelong13; Wang, Fei-Yue1,14; Wang, Jue; Wang, Ping2; Wei, Junqing3
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2023-02-01
卷号8期号:2页码:1156-1175
ISSN号2379-8858
关键词Cameras Laser radar Autonomous vehicles Roads Annotations Trajectory Indexing Driving scenario data storage data indexing
DOI10.1109/TIV.2022.3215503
通讯作者Li, Li(li-li@tsinghua.edu.cn)
英文摘要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, we propose MetaScenario, a framework for driving scenario data. We 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. We 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.
WOS关键词CLASSIFICATION ; INTELLIGENCE ; STRATEGY ; VEHICLES
资助项目National Key Research and Development Program of China[2020AAA0108104] ; Science and Technology Innovation Committee of Shenzhen[CJGJZD20200617102801005]
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000967428300001
资助机构National Key Research and Development Program of China ; Science and Technology Innovation Committee of Shenzhen
源URL[http://ir.ia.ac.cn/handle/173211/53744]  
专题多模态人工智能系统全国重点实验室
通讯作者Li, Li
作者单位1.Qingdao Acad Intelligent Ind, Qingdao 266000, Peoples R China
2.Peking Univ, Beijing 100084, Peoples R China
3.DiDi Autonomous Driving Co, Beijing 100094, Peoples R China
4.Uisee Technol Beijing Co Ltd, Beijing 100028, Peoples R China
5.Intel Labs China, Beijing 100190, Peoples R China
6.Chinese Acad Sci, Inst Automat, Momenta & Natl Lab Pattern Recognit, Beijing 100084, Peoples R China
7.Tsinghua Univ, Dept Automat, BNRist, Beijing 100084, Peoples R China
8.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot IAIR, Xian 710049, Peoples R China
9.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
10.Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada
推荐引用方式
GB/T 7714		 Chang, Cheng,Cao, Dongpu,Chen, Long,et al. MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(2):1156-1175.
APA Chang, Cheng.,Cao, Dongpu.,Chen, Long.,Su, Kui.,Su, Kuifeng.,...&Li, Li.(2023).MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(2),1156-1175.
MLA Chang, Cheng,et al."MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.2(2023):1156-1175.

入库方式： OAI收割

来源：自动化研究所

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