[1]赵祥模国家重点研发计划(0YFB000)团队.自动驾驶测试与评价技术研究进展[J].交通运输工程学报,2023,23(06):10-77.[doi:10.19818/j.cnki.1671-1637.2023.06.002]
　ZHAO Xiang-mo's team supported by the National Key Research and Development Program of China(0YFB000).Research progress in testing and evaluation technologies for autonomous driving[J].Journal of Traffic and Transportation Engineering,2023,23(06):10-77.[doi:10.19818/j.cnki.1671-1637.2023.06.002]
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自动驾驶测试与评价技术研究进展()
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《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

卷:
第23卷
期数:
2023年06期
页码:
10-77
栏目:
综述
出版日期:
2023-12-30

文章信息/Info

Title:
Research progress in testing and evaluation technologies for autonomous driving
文章编号:
1671-1637(2023)06-0010-68
作者:
赵祥模国家重点研发计划(2021YFB2501200)团队
Author(s):
ZHAO Xiang-mo's team supported by the National Key Research and Development Program of China(2021YFB2501200)
关键词:
智能交通;  自动驾驶;  测试与评价技术;  交通流仿真测试;  硬件在环测试;  场地测试;  智能性评价;  测试评价工具链;  缺陷检测
Keywords:
intelligent transportation;  autonomous driving;  test and evaluation technology;  traffic flow simulation test;  hardware-in-the-loop test;  field test;  intelligence evaluation;  test evaluation tool chain;  defect detection
分类号:
U495
DOI:
10.19818/j.cnki.1671-1637.2023.06.002
文献标志码:
A
摘要:
针对实际复杂交通运行环境中自动驾驶车辆整车级测试成本高、周期长、覆盖度低、缺乏完善工具链等难题,分析了自动驾驶测试与评价技术7大领域的研究现状,展望了未来发展方向,具体涵盖了自动驾驶车辆仿真测试技术、交通流仿真测试技术、硬件在环测试技术、场地测试技术、智能性评价技术、测试评价工具链与体系构建、认证与潜在缺陷检测技术等。在自动驾驶车辆仿真测试方面,分析了自动驾驶仿真测试软件、车辆动力学模型、测试背景车交互行为模型、云控平台监管的仿真测试与车辆仿真系统标准化的研究现状,总结了自动驾驶车辆仿真测试目前存在的主要问题; 在自动驾驶交通流仿真测试方面,总结了测试背景车驾驶风格模型、交通流建模与仿真、交通场景生成方法和加速测试方法的研究现状,展望了自动驾驶交通流仿真测试的未来发展趋势; 在硬件在环测试技术方面,总结了人-车-路-环多维数字孪生测试和自动驾驶车辆整车级系统平台构建方法,概述了高清摄像头、毫米波雷达、超声波雷达等典型传感器数据与车车/车路通信信号的模拟技术; 在场地测试技术方面,综述了封闭场地测试、开放道路测试与高速公路测试相关测试场、测试标准和关键技术的发展现状; 在智能性评价技术方面,从自动驾驶智能性概念、场景复杂度量化和评估、自动驾驶智能性评价体系与社会合作性评价方法四方面介绍自动驾驶智能性评价方法的研究现状; 在测试评价工具链与体系构建方面,主要从测试评价工具链技术、自动驾驶测试评价体系、自动驾驶测试标准现状三方面介绍了自动驾驶测试评价标准体系现状; 在认证与潜在缺陷检测技术方面,从自动驾驶缺陷的定义、致因分析、缺陷分类、缺陷检测等方面综述了目前自动驾驶的缺陷检测方法,总结了自动驾驶车辆安全保障面临的挑战。研究结果表明:自动驾驶测试评价技术虽已取得较大进展,但测试评价标准体系仍不完善,现有测试工具与方法难以满足L3级及以上自动驾驶车辆测试需求; 虚拟仿真与数字孪生技术发展应用水平较低,在拟真度、测试效率与整车级测试能力方面存在诸多不足; 未来需进一步加强全场景、高保真建模技术与实时仿真软件研发,建立虚实交互的在线加速孪生测试系统,研究自动驾驶全栈危险测试场景生成和加速测试方法,整合自动驾驶测试技术和工具,形成自动驾驶测试评价的工具链,完善标准规范。
Abstract:
In view of the high cost, long cycle, low coverage, and lack of a perfect tool chain for autonomous driving vehicle tests in the actual complex traffic environment, the research status of seven major areas of testing and evaluation technologies for autonomous driving was analyzed, and the future development direction of testing and evaluation technologies for autonomous driving was predicted, including simulation and testing technology of autonomous driving vehicles, simulation and testing technology of traffic flow, hardware-in-the-loop testing technology, field testing technology, intelligence evaluation technology, testing and evaluation tool chain and its system's construction, certification and potential defect detection technology, etc. In terms of simulation and testing of autonomous driving vehicles, the research status of simulation and testing software for autonomous driving, vehicle dynamics models, background vehicle interaction behavior models for testing, simulation and testing of cloud control platform supervision, and standardization of vehicle simulation system was analyzed. The main problems currently existing in the simulation and testing of autonomous driving vehicles were summarized. In terms of simulation and testing of autonomous driving traffic flow, the research status of driving style models for test background vehicles, traffic flow modeling and simulation, traffic scenario generation methods, and acceleration testing methods was summarized, and the future development trend of simulation and testing of autonomous driving traffic flow was predicted. In terms of hardware-in-the-loop testing technology, the human-vehicle-road-loop multi-dimensional digital twin tests and construction methods of the system platform for autonomous driving vehicles were summarized. Typical sensor data from high-definition cameras, millimeter-wave radars, and ultrasonic radars, as well as simulation technology of vehicle-to-vehicle and vehicle-to-road communication signals, were reviewed. In terms of field testing technology, the development status of closed field testing, open road testing, and highway test-related testing fields, testing standards, and key technologies were summarized. In terms of intelligence evaluation technology, the research status of intelligence evaluation methods for autonomous driving was introduced from four aspects: the concept of autonomous driving intelligence, the quantification and evaluation of scene complexity, the intelligence evaluation systems of autonomous driving, and the social cooperation evaluation methods. In terms of testing and evaluation tool chain and system construction, the current situation of the testing and evaluation standard system for autonomous driving was introduced mainly from three aspects: the testing and evaluation tool chain technology, the autonomous driving testing evaluation system, and the current situation of autonomous driving testing standards. Finally, in terms of certification and potential defect detection technology, the current defect detection methods for autonomous driving were reviewed from the definition, cause, classification, and detection of autonomous driving defects. The challenges faced in the safety assurance of autonomous driving vehicles were summarized. Research results show that although the autonomous driving testing and evaluation technology has made great progress, the testing and evaluation standard system is still not perfect, and the existing testing tools and methods fail to meet the testing needs of autonomous driving vehicles at level three and above. The development and application level of virtual simulation and digital twin technology is low, and there are many deficiencies in the degree of simulation, testing efficiency, and vehicle testing ability. In the future, it is necessary to further strengthen the research and development of full-scene and high-fidelity modeling technology and real-time simulation software, establish an online accelerated twin testing system with virtual and real interaction, study the scenario generation and acceleration methods of autonomous driving full-stack hazardous testing, and integrate autonomous driving testing technologies and tools, so as to form a tool chain for autonomous driving testing and evaluation and improve standard specifications.20 tabs, 29 figs, 299 refs.

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备注/Memo

备注/Memo:
收稿日期:2023-06-19
基金项目:国家重点研发计划(2021YFB2501200)
作者简介:赵祥模(1966-),男,重庆人,长安大学教授,工学博士,从事智慧交通与智能网联汽车测试技术研究。
更新日期/Last Update: 2023-12-30