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Journal of Software:2021.32(4):973-987

时空轨迹数据驱动的自动驾驶场景元建模方法
张梦寒,杜德慧,张铭茁,张雷,王耀,周文韬
(华东师范大学 软件工程学院, 上海 200062;上海市高可信重点实验室(华东师范大学), 上海 200062;教育部可信软件国际合作联合实验室(华东师范大学), 上海 200062;同济大学 上海自主智能无人系统科学中心, 上海 201210)
Spatio-temporal Trajectory Data-driven Autonomous Driving Scenario Meta-modeling Approach
ZHANG Meng-Han,DU De-Hui,ZHANG Ming-Zhuo,ZHANG Lei,WANG Yao,ZHOU Wen-Tao
(Software Engineering Institute, East China Normal University, Shanghai 200062, China;Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China;International Joint Laboratory of Trustworthy Software of Ministry of Education (East China Normal University), Shanghai 200062, China;Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China)
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Received:September 13, 2020    Revised:October 26, 2020
> 中文摘要: 时空轨迹数据驱动的汽车自动驾驶场景建模,是当前汽车自动驾驶领域中驾驶场景建模、仿真所面临的关键问题,对于提高系统的安全性具有重要的研究意义.近年来,随着时空轨迹数据建模及应用研究的快速发展,时空轨迹数据应用于特定领域建模的研究引起人们的广泛关注.但是,由于时空轨迹数据所反映的现实世界的多元性和复杂性以及时空轨迹数据的海量、异构、动态等特点,基于时空轨迹数据驱动的安全攸关场景建模的研究仍面临着挑战,包括:统一的时空轨迹数据元模型、基于时空轨迹数据的元建模方法、基于数据分析技术的时空轨迹数据处理、数据质量评价等.针对汽车自动驾驶领域的场景建模需求,提出一种基于MOF元建模体系构建时空轨迹数据的元建模方法,根据时空轨迹数据的特征及自动驾驶的领域知识,构建了面向汽车自动驾驶的时空轨迹数据元模型;基于此,提出基于时空轨迹数据元建模技术体系的自动驾驶安全场景建模方法,并使用场景建模语言ADSML实例化安全场景,构建安全场景库,旨在为此类系统的安全关键场景建模提供一种可行的方案.结合变道超车场景的案例,展示了时空轨迹数据驱动的自动驾驶安全场景元建模方法的可用性,为场景模型的构建、仿真、分析奠定了基础.
Abstract:In the current autonomous driving scenario modeling and simulation field, spatio-temporal trajectory data-driven modeling and application of autonomous driving safety-critical scenario are key problems, which is significant to improve the security of the system. In recent years, great progress has been achieved in the modeling and application of spatio-temporal trajectory data, and the application of spatio-temporal trajectory data in specific fields has attracted wide attention. However, due to spatio-temporal trajectory data has diversity and complexity as well as massive, heterogeneous, dynamic characteristics, researches in the safety-critical field modeling still face challenges, including unified meta-data of spatio-temporal trajectory, meta-modeling method based on spatio-temporal trajectory data, data processing based on the data analysis of spatio-temporal trajectory, and data quality evaluation. In view of the scenario modeling requirements in the field of autonomous driving, a meta-modeling approach is proposed to construct spatio-temporal trajectory meta-data based on MOF meta-modeling system. According to the characteristics of spatio-temporal trajectory data and autonomous driving domain knowledge, a meta-model of spatio-temporal trajectory data is constructed. Then, the modeling approach of autonomous driving safety-critical scenarios is studied based on spatio-temporal trajectory data element modeling technology system, a scenario modeling language ADSML is used to automatic instantiation safety-critical scenarios, and a library of safety-critical scenarios is constructed, aiming to provide a feasible approach for the modeling of such safety-critical scenarios. Combined with the scenario of lane change and overtaking, the effectiveness of spatio-temporal trajectory data-driven autonomous driving safety-critical scenario meta-modeling approach is demonstrated, which lays a solid foundation for the construction, simulation, and analysis of the scene model.
文章编号:     中图分类号:TP311    文献标志码:
基金项目:国家自然科学基金(61972153);国家重点研发计划(2018YFE0101000);科技部重点项目(2020AAA0107800) 国家自然科学基金(61972153);国家重点研发计划(2018YFE0101000);科技部重点项目(2020AAA0107800)
Foundation items:National Natural Science Foundation of China (61972153); National Key R&D Program of China (2018YFE 0101000); Key Projects of the Ministry of Science and Technology (2020AAA0107800)
Author NameAffiliationE-mail
ZHANG Meng-Han Software Engineering Institute, East China Normal University, Shanghai 200062, China
Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China
International Joint Laboratory of Trustworthy Software of Ministry of Education (East China Normal University), Shanghai 200062, China 
 
DU De-Hui Software Engineering Institute, East China Normal University, Shanghai 200062, China
Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China
International Joint Laboratory of Trustworthy Software of Ministry of Education (East China Normal University), Shanghai 200062, China 
dhdu@sei.ecnu.edu.cn 
ZHANG Ming-Zhuo Software Engineering Institute, East China Normal University, Shanghai 200062, China
Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China
International Joint Laboratory of Trustworthy Software of Ministry of Education (East China Normal University), Shanghai 200062, China 
 
ZHANG Lei Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210, China  
WANG Yao Software Engineering Institute, East China Normal University, Shanghai 200062, China
Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China
International Joint Laboratory of Trustworthy Software of Ministry of Education (East China Normal University), Shanghai 200062, China 
 
ZHOU Wen-Tao Software Engineering Institute, East China Normal University, Shanghai 200062, China
Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China
International Joint Laboratory of Trustworthy Software of Ministry of Education (East China Normal University), Shanghai 200062, China 
 
Reference text:

张梦寒,杜德慧,张铭茁,张雷,王耀,周文韬.时空轨迹数据驱动的自动驾驶场景元建模方法.软件学报,2021,32(4):973-987

ZHANG Meng-Han,DU De-Hui,ZHANG Ming-Zhuo,ZHANG Lei,WANG Yao,ZHOU Wen-Tao.Spatio-temporal Trajectory Data-driven Autonomous Driving Scenario Meta-modeling Approach.Journal of Software,2021,32(4):973-987