摘要
随着自动驾驶测试验证对虚拟仿真场景依赖程度的增加,传统基于专家经验的场景枚举生成方法已无法满足测试需求。数字虚拟仿真场景自动生成方法在场景多样性、危险性、可解释性、生成效率等方面存在巨大技术优势,是提高汽车自动驾驶技术测试验证安全性和可靠性的关键,已成为当前汽车智能化领域的研究热点。在广泛调研场景自动生成方法领域研究成果的基础上,系统地梳理了场景定义、场景解构、基于机理建模的场景生成、数据驱动的场景生成等方向中最新的研究进展,重点分析了一些值得深入研究的问题,最后对未来可能的研究方向进行展望。场景解构方面,针对场景具有无限丰富、极其复杂、不可穷举特征的问题,应重点关注“场地-气象-交通”耦合的多源异构复杂场景解构方法研究;基于机理建模的场景生成方面,针对多样性、边界性的测试需求,重点关注场景组合生成研究、边界场景优化生成及自适应生成研究等方面;基于数据驱动的场景生成方面,采集内容丰富的数据集是研究的基础,应充分挖掘场景数据的测试价值,重点关注场景重构、加速测试的场景采样、危险场景衍生等方面的研究。未来的研究应重点关注以上几个方面,建立完整的自动驾驶虚拟仿真场景自动生成体系,为L4级及以上的高等级自动驾驶大规模仿真测试评估奠定理论基础。
The traditional scenario enumeration method based on expert experience has failed to meet testing requirements owing to the increasing reliance of autonomous driving on virtual simulation scenarios for testing and verification.The automatic generation of simulation scenarios has substantial technical advantages in terms of scenario diversity,safety,interpretability,and generation efficiency.It plays a crucial role in improving the efficiency of autonomous driving tests,which have become a prevalent research topic.In recent years,researchers have intensively studied automatic scenario generation methods.In the present study,extensive research was conducted on the results obtained in the field of automatic scenario generation.Thus,the latest research progress in scenario definition,scenario deconstruction,scenario generation based on mechanism modeling,scenario generation driven by data,etc.,is schematically presented in this paper.In addition,an analysis on some areas worthy of further study was performed,and prospective research directions are presented herein.In terms of scenario deconstruction,given that scenarios are abundant,extremely complex,and inexhaustible,substantial importance should be given to research on the deconstruction of heterogeneous complex scenarios with the coupling of“field-weather-traffic.”Regarding mechanism modeling,to meet the requirements of testing scenario diversity and boundary generation,the focus should be on scenario combination generation,edge scenario optimization generation,and adaptive generation.Furthermore,data with rich content must be collected,laying the foundation for research.To fully exploit the test value of scenario data,attention should be paid to the research on scenario reconstruction,thereby accelerating the generation of test scenario databases and dangerous scenarios.Thus,future research should focus on the aspects mentioned above to establish a completely automatic simulation scenario generation system for autonomous driving.This will lay a theoretical fo
作者
邓伟文
李江坤
任秉韬
王文奇
丁娟
DENG Wei-wen;LI Jiang-kun;REN Bing-tao;WANG Wen-qi;DING Juan(School of Transportation Science&Engineering,Beihang University,Beijing 100191,China;Beijing Advanced Innovation Center for Big Data and Brain Computing,Beihang University,Beijing 100191,China;Zhejiang Tianxingjian Intelligent Technology Co.Ltd.,Jiaxing 314000,Zhejiang,China)
出处
《中国公路学报》
EI
CAS
CSCD
北大核心
2022年第1期316-333,共18页
China Journal of Highway and Transport
基金
国家重点研发计划项目(2018YFB0105103)
国家自然科学基金项目(U1864201)
北京市自然科学基金项目(3204046)。
关键词
汽车工程
仿真场景
综述
自动生成
自动驾驶
场景解构
机理建模
数据驱动
automotive engineering
simulation scenarios
review
automatic generation
autonomous driving
scenario deconstruction
mechanism modeling
data driven modeling
