Research progress in testing and evaluation technologies for autonomous driving
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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.
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图 3 自动驾驶车辆仿真测试技术组织结构
Figure 3. Organizational structure of simulation and testing technology of autonomous driving vehicles
图 8 C-ASAM工作组研究框架与内容拓展
Figure 8. Research framework and content expansion of C-ASAM working group
图 9 自动驾驶交通流仿真测试技术组织结构
Figure 9. Organizational structure of simulationand testing technology of autonomous driving traffic flow
图 12 传统仿真基于车道的建模描述
Figure 12. Lane-based modeling description of traditional simulation
图 13 现实中非结构化道路处基于面域运动的交通流
Figure 13. Traffic flow based on area movement on unstructured roads in reality
图 14 硬件在环测试技术组织结构
Figure 14. Organizational structure of hardware-in-the-loop testing technology
图 15 自动驾驶场地测试技术组织结构
Figure 15. Organizational structure of field testing technology of autonomous driving
图 19 自动驾驶智能性评价方法组织结构
Figure 19. Organizational structure of intelligence evaluation method of autonomous driving
图 21 基于场景复杂度的车辆智能性评价体系
Figure 21. Vehicle intelligence evaluation system based on scenario complexity
图 22 自动驾驶车辆的行驶自治性、社会合作性和学习进化性
Figure 22. Driving autonomy, social cooperation and learning evolution of autonomous driving vehicles
图 24 近十年自动驾驶标准发布数量趋势
Figure 24. Trend of issued number of autonomous driving standards in the past decade
图 26 自动驾驶车辆认证与潜在缺陷检测技术组织结构
Figure 26. Organizational structure of certification and potential defect detection technology of autonomous driving vehicles
图 28 对自车运动决策构成危险的几种情形
Figure 28. Several situations posing risks to movement decision-making of autonomous driving vehicles
图 29 对场景风险因子和交通物理原理交叉分析得到的交通干扰场景
Figure 29. Traffic interference scenarios obtained from cross analysis of scenario risk factors and traffic physics principles
表 1 三种自动驾驶测试方法优缺点对比
Table 1. Comparison of advantages and disadvantages of three autonomous driving test methods
方法 仿真测试 封闭场地测试 开放道路测试 对象 软件、硬件、虚拟环境 真实车辆、实际道路、模拟参与者 真实车辆、真实道路、真实参与者 优点 虚拟的测试环境；安全；丰富的测试场景；可重复且测试效率高 真实车辆和道路；封闭场景单一；可重复测试；安全可控 真实的车辆、道路和参与者；连续多变的测试场景；贴合实际应用 缺点 依赖车辆动力学模型；精度不高；无法验证车辆的执行能力 建设场地费用较大；测试场景数量有限；可升级性不高 路况不可控，安全风险大；需要大量的时间和成本投入；可重复性差 
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表 2 主要仿真测试工具及其特点
Table 2. Main simulation test tools and their characteristics
主要目的 软件 所属公司 车辆动力学模型 传感器与环境模型 交通参与者模型 车辆动力学仿真 CarSim MSC 好 有 有 CarMaker IPG 好 有 有 交通流仿真 VISSIM PTV 无 无 良 SUMO DLR 无 无 良 ADAS仿真测试 PreScan TASS 有 良 有 VTD VIRES 有 良 有 SCANeR OKTAL 有 良 有 Panosim 天行健 良 良 有 CARLA Intel 无 有 有 自动驾驶仿真测试 Carcraft Waymo 有 好 良 TAD Sim 腾讯 有 好 良 51Sim-One 51WORLD 有 好 良 Apollo 百度 有 好 良
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表 3 不同控制模型优缺点
Table 3. Advantages and disadvantages of different control models
模型类型 车辆模型 优点 缺点 几何模型 基于阿克曼转向的几何模型 简单，只需少量参数；有效描述车辆位置与路径的关系；控制器设计简单 未考虑车辆运动学与动力学特性 运动学模型 全车运动学模型 考虑到相对于局部坐标和全局坐标的车辆航向之间存在不同方向的可能性 未考虑车辆动力学特性；略微比几何模型复杂 半车运动学模型(自行车模型) 模型简单，便于控制器的设计 未考虑车辆动力学特性；假设车辆相对于局部坐标的航向与相对于全局坐标的航向相同 带侧偏角的运动学模型 模型中考虑车辆的侧偏特性 未考虑车辆动力学特性；增加了建模复杂度 动力学模型(线性) 全车动力学模型 考虑了所有车轮的力，尤其是在转向运动中 未考虑轮胎的非线性特性 半车动力学模型(自行车模型) 考虑了车辆的动力学特性，较为常用 未考虑轮胎的非线性特性；忽略左右车轮不同响应带来的影响 动力学模型(非线性) 全车动力学模型 考虑了所有车轮的力，尤其是在转向运动中；考虑轮胎响应相对于侧偏角的非线性 模型复杂，不能保证控制的实时性 半车动力学模型(自行车模型) 考虑轮胎响应相对于侧偏角的非线性 未考虑转向时内外侧车轮的差异带来的影响
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表 4 自动驾驶封闭测试场地建设类别
Table 4. Construction categories of closed test fields for autonomous driving
场地名称 自动加速与制动测试场地、自动转向“S”型路线测试场地、弯道行驶测试场地、坡道停车和起步测试场地、道路入口测试场地、道路出口测试场地 人行横道减速测试场地、减速丘限速测试场地、道路限速测试场地、施工区测试场地、停车让行测试场地、减速让行测试场地、锥形交通路标测试场地 人行横道信号灯识别与响应测试场地、平面交叉口场景测试场地、进出环岛测试场地 隧道模拟测试场地、前方车辆静止测试场地、前方车辆紧急制动测试场地、前方车辆减速测试场地、主动换道测试场地、前方行人横穿测试场地 测试用途 基于不同的道路类型测试自动驾驶 基于不同的交通标志标线及其他道路交通设施测试自动驾驶 基于交叉口、环岛及信号灯等交通设施测试自动驾驶 测试其他交通参与者及定位信号遮蔽道路对自动驾驶的影响
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表 5 国外主要智能网联汽车试验场对比分析
Table 5. Comparative analysis of major foreign connected and automated vehicle test fields
名称 国家 占地面积/104m2 建成时间 测试功能 特点分析 Mcity 美国 13.0 2015年 自动驾驶技术、V2X技术 强化试验，柔性化设计 Asta Zero 瑞典 200.0 2014年 车辆动力学、驾驶人行为、V2X技术 ADAS场景测试与模拟设备，具备完整的测试功能 Smart Road 美国 20世纪80年代 自动驾驶技术、智能交通系统、V2X技术 天气模拟系统、照明和能见度检测系统 City Circuit 英国 304.0 传统汽车、智能交通系统、智能网联汽车测试 网联汽车测试设备、跟踪定位与监控设备 Gomentum Station 美国 850.0 2014年 自动驾驶技术、V2X技术 2条真实的隧道，测试面积大 Castle Air Force Base 美国 24.3 2011年 自动驾驶技术 Willow Run 美国 136.0 2018年 自动驾驶技术、V2X技术 天然坑洞与3层立交桥
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表 6 中国关于智能网联汽车开放道路测试的相关标准、法规与政策
Table 6. Standards, regulations, and policies related to open road testing of connected and automated vehicles in China
时间 标准、法规与政策 2017.12 《北京市自动驾驶车辆道路测试管理实施细则(试行)》 2018.03 《上海市智能网联汽车道路测试管理办法(试行)》 2018.03 《重庆市自动驾驶道路测试管理实施细则(试行)》 2018.04 《智能网联汽车道路测试管理规范(试行)》 2018.08 《杭州市智能网联车辆道路测试管理实施细则(试行)》 2018.10 《深圳市智能网联汽车道路测试开放道路技术要求(试行)》 2018.12 广州市《关于智能网联汽车道路测试有关工作的指导意见》 2019.10 《自动驾驶车辆道路测试安全管理规范》 2021.07 《智能网联汽车道路测试与示范应用管理规范(试行)》 2022.10 《道路车辆自动驾驶系统测试场景词汇》 2023.11 《四部委关于开展智能网联汽车准入和上路通行试点工作的通知》
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表 7 欧美各国关于智能网联汽车开放道路测试的相关标准、法规与政策
Table 7. Standards, regulations, and policies related to open road testing of connected and automated vehicles in European and American countries
时间 国家 标准、法规与政策 2016.09 美国 《联邦自动驾驶汽车政策指南》 2017.05 德国 《自动驾驶法》 2016.05 日本 《自动驾驶汽车道路测试指南》 2015.07 英国 《自动驾驶汽车发展道路：道路测试指南》 2017.05 瑞典 《自动驾驶汽车公共道路测试规范》
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表 8 自动驾驶车辆安全性、舒适性与高效性研究
Table 8. Research on safety, comfort and efficiency of autonomous driving vehicles
角度 安全性 舒适性 高效性 相关文献 开发设计 $ \checkmark$ [211]、[212] $ \checkmark$ [213] $ \checkmark$ [214] $ \checkmark$ $ \checkmark$ [215] $ \checkmark$ $ \checkmark$ [216]、[217] 测试评价 $ \checkmark$ [218] $ \checkmark$ [219] $ \checkmark$ [220] $ \checkmark$ $ \checkmark$ [221] $ \checkmark$ $ \checkmark$ [222] $ \checkmark$ $ \checkmark$ $ \checkmark$ [200]
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表 9 自动驾驶测试场景中复杂度的量化方法
Table 9. Quantitative methods of complexity in autonomous driving testing scenarios
复杂度定义视角 量化方法 主/客观 量化要素 数据获取视角 文献 基于场景内部构成描述并量化复杂度 Multi-Factor Analysis、Survey 主观 ● ★ ◆ 车外 [231] AHP 主观 ■ ◆ 车外 [195] Ontology、AHP 主客观结合 ◆ 车外、自车 [238] 主客观结合 ▲★ 车外、自车 [238] 基于场景外部观测定义并量化复杂度 SVR 主客观结合 ● ■ ◆ 车外 [235] Survey 主观 ● ■ ◆ 车外、自车 [234] Random Forest 主客观结合 ● ◆ 自车 [236] NASA-TLX Surveys 主客观结合 ● ★ ■ 自车 [237] Drivable Area、Entropy 客观 ● ▲ ★ ◆ 车外 [232]
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表 10 不同比赛中自动驾驶车辆智能度评价方法对比
Table 10. Comparison of intelligence evaluation methods of autonomous driving vehicles in different competitions
比赛/项目 国家 首届时间 考察功能或性能 指标 Percept OR 美国 2001年 自主行为能力 自主行驶里程、任务耗时、行驶速度、人工干预次数、任务失败次数 Grand Challenge 美国 2004年 自主行为能力 行驶里程、完成时间 Urban Challenge 美国 2007年 自主行为能力和部分交互能力 任务完成时间、完成质量、违反交通规则、危险行为 智能车未来挑战赛 中国 2009年 安全性、舒适性、敏捷性、智能性 完成任务总时间、任务完成质量、人工干预次数 自动驾驶车辆挑战赛 中国 2018年 智能性、安全性、人机交互、能效 任务完成时间、任务完成度、人工干预次数
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表 11 不同研究中自动驾驶车辆智能度评价方法对比
Table 11. Comparison of intelligence evaluation methods of autonomous driving vehicles in different studies
来源 机构 评价目标/准则/指标 类型 Lowrie等[239] 马里兰大学 总行驶里程、人工控制行为、人工干涉率等 基于里程 Pomerleau等[240] 卡内基梅隆大学 基于里程 Broggi等[241] 帕尔马大学 基于里程 Maurer等[242] 慕尼黑国防大学 基于里程 美国国家标准与技术研究院[243] 人工干预度、环境复杂度、任务复杂度 基于里程 Wang等[244] 中国科学院沈阳自动化研究所 感知技术、导航技术、人机交互、信息通讯技术、路径规划技术、多平台协同技术、运动控制技术、任务规划、学习适应 基于场景 Sun等[245] 北京理工大学 车辆控制行为、基本和高级行车行为、基本和高级交通行为 基于场景 蒙昊蓝等 同济大学 行驶自治性、社会合作性、学习进化性 基于场景
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表 12 背景车模型的分类与优势分析
Table 12. Classification and advantage analysis of background vehicle models
控制方式 既定轨迹 跟驰模型 驾驶行为模型 可解释性 参数化、可解释 参数化、可解释 无解析表达 状态空间复杂度 极低 低、确定性 高、概率性 交互能力 无 纵向 纵向、侧向 优势 完全可重复性高 可解释性较强 类人决策实现多种驾驶风格或动机
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表 13 各国自动驾驶测试工具链集成方案
Table 13. Tool chain integration schemes for autonomous driving test in various countries
序号 名称 国家 合作单位 特征 1 Test Bed Lower Saxony for Automated and Connected Mobility 德国 德国航天中心、德国交通部、德国西门子、大众、沃尔沃 覆盖城乡、高速公路等多种道路，支持硬件、软件、驾驶人与车辆在环测试工具的自动驾驶测试系统 2 VTD 美国 德国VIRES 提供复杂交通场景与物理驱动模拟，包含软硬件、驾驶人与车辆在环的测试环境 3 ASAM OpenX 德国 ASAM 提供驾驶和交通模拟器界面，包含用于验证自动驾驶功能的静态和动态内容 4 Drive Sim/Drive Constellation 美国 英伟达 围绕车端、桌面端、云端构建了GPU硬件统一架构和CUDA软件架构，为自动驾驶应用领域提供支持 5 Apollo 中国 百度 包括完整的软硬件和服务系统，包括车辆平台、硬件平台、云端数据服务等；场景库包括了法规标准场景、危险工况场景和能力评估场景共200种 6 华为八爪鱼(HUAWEI Octopus) 中国 华为 在数据采集、数据挖掘、数据标注、算法训练、仿真平台等方面提供完整解决方案，包含大量数据集和场景库 7 TAD Sim 中国 腾讯 通过工业级车辆动力学模型、虚实结合等技术打造虚实结合、线上线下一体的自动驾驶仿真平台
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表 14 各国自动驾驶测试评价相关项目
Table 14. Projects related to autonomous driving test and evaluation in various countries
序号 项目/机构 国家 时间 方法 核心 测试目标 测试工具 1 Waymo 美国 2013年 虚拟仿真测试 模拟各种复杂路况 提升测试里程数与加速测试 基于Carcraft的虚拟仿真测试 2 AdaptIVe 欧洲 2014年 面向L2级以上实车测试 侧重于用户体验相关指标的研究 研究车载传感器与车对车互联、V2X 开放道路测试 3 Cruise 美国 2016年 虚拟仿真测试 现实场景转化到可以编辑的虚拟场景 推进L4级自动驾驶车辆测试 基于Morpheus的虚拟仿真测试 4 PEGASUS 德国 2016年 虚拟仿真测试 通过虚拟仿真环境对自动驾驶技术进行测试和验证 提高自动驾驶车辆安全性 基于研发的虚拟仿真软件 5 BMW 德国 2017年 面向L4级虚拟仿真测试与实车测试 通过数据驱动进行严格的测试 提高自动驾驶车辆安全性 基于Unity的虚拟仿真测试与开放道路测试 6 TRI 日本 2017年 实车测试 高级驾驶辅助模式和完全自动驾驶模式的综合测试 防止车辆直接撞向前方物体，探测驾驶人行为 开放道路测试 7 车路协同联合实验室 中国 2018年 实车测试 以车辆的需求为主体，传送不同的信息给不同的车辆 研究CAV与路侧基站的信息感知及交互能力，CAV开启与否对AEB功能的影响 开放道路测试 8 中国科学院自动化研究所 中国 2019年 虚拟仿真测试与实车测试 提出一种新的图灵测试方法测试无人车对复杂场景的理解和决策能力 研究无人车对复杂交通场景的理解与决策的能力 基于无人驾驶测试与验证系统的虚拟仿真测试与封闭道路测试 9 长安大学 中国 2021年 自动驾驶仿真与数字孪生测试评价工具链 面向演进交通环境下的自动驾驶上路许可与效能评价 加速突破适应演进交通环境的高等级自动驾驶系统上路许可与效能评价中的关键技术，开发面向智能评级、缺陷识别及安全认证的完整工具链 实时仿真软件、加速仿真云平台、场地孪生测试系统、整车交通在环测试平台与综合效能评价体系 10 百度Apollo 中国 2021年 面向L3/L4级虚拟仿真测试与实车测试 侧重于实现量产园区自动驾驶、限定区域城市自动驾驶、城市全网自动驾驶 研究使用纯路侧感知能力，真正实现开放道路连续路网L4级别自动驾驶闭环的车路协同技术 基于Apollo Studio虚拟仿真测试与开放道路测试
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表 15 各国近十年发布的关于自动驾驶的法规
Table 15. Regulations on autonomous driving issued by various countries in past decade
国家 法规 时间 机构/地点 主要内容 中国 《北京市自动驾驶车辆道路测试管理实施细则(试行)》 2017年 北京市交通委员会、北京市公安交通管理局、北京市经济和信息化局 规定了自动驾驶的研发和测试中对测试单位申请条件、车辆要求与驾驶要求等 《智能网联汽车道路测试管理规范(试行)》 2018年 工信部、公安部、交通运输部 发布智能网联汽车发展技术路线图，制定了发展目标和战略规划 《智能网联汽车自动驾驶功能测试规程(试行)》 2018年 智能网联汽车产业创新联盟、全国汽车标准化技术委员会智能网联汽车分技术委员会 规定了14个自动驾驶功能与34个场景 《智能网联汽车道路测试与示范应用管理规范(试行)》 2021年 交通运输部、工信部、公安部 主要明确了道路测试、示范应用及测试区(场)的定义，将道路测试和示范应用的范围扩展到包括高速公路在内的公路、城市道路和区域，并对省、市级相关主管部门的主要职责与工作机制进行了说明 《自动驾驶汽车运输安全服务指南(试行)》 2022年 交通运输部 要求从事运输经营的自动驾驶车辆应当具备车辆运行状态记录、存储和传输功能，向运输经营者和属地交通运输主管部门及时传输相关信息 《道路车辆动驾驶系统测试场景词汇》 2022年 工信部 规范了自动驾驶系统、动态驾驶任务、设计运行范围及条件等概念，明确了场景、动静态环境与实体要素之间的关系，与功能安全、预期功能安全等国际标准建立了配套关系 美国 《自动驾驶法案(H.R. 3388)》 2017年 众议院 首次对自动驾驶车辆的生产、测试和发布进行管理 《自动驾驶车辆法》 2021年 亚利桑那州 只要车内有驾驶人，L3和L4级就可以在亚利桑那州的公共道路上行驶 《自动驾驶客运服务试验和部署计划书》 2021年 加利福尼亚州 将自动驾驶所有活动分为4种，并形成4种不同计划的监管规则 德国 《道路交通法第八修正案》 2017年 联邦议院 允许L3级自动驾驶上路行驶，规定记录装置，明确交通事故责任 《道路交通法修正案(自动驾驶法)》 2021年 联邦政府、联邦议院、参议院 允许自动驾驶L4级车辆在特定场景下和特定区域内行驶 日本 《自动驾驶汽车道路测试指南》 2016年 警察厅 要求驾驶人须坐在驾驶位置上进行测试并遵守相应法律法规，且先进行封闭试验场地测试 《道路交通法》 2019年 众议院、政府内阁 允许L3级自动驾驶上路行驶 英国 《自动驾驶车辆发展道路：无人驾驶技术规则综述》 2016年 商务部、交通运输部 支持Google、捷豹、沃尔沃、日产等自动驾驶车辆制造厂商在英国进行公共道路测试 《汽车技术和航空法案》 2017年 交通运输部 规定了自动驾驶车辆发生事故时的责任分配问题以及影响责任判定的因素 《自动与电动汽车法案》 2018年 交通运输部 规定了自动驾驶车辆引起的交通事故保险赔付方法 《自动驾驶联合报道》 2022年 英格兰和威尔士法律委员会、苏格兰法律委员会 提出75条自动驾驶法规方面的建议
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表 16 自动驾驶测试标准包含的测试功能
Table 16. Test functions included in autonomous driving test standards
标准 功能 ACC FCW 变道决策 盲区检测 LKA 车道偏离预警 AEB 低速跟踪 ISO 17387：2008 √ ISO 22178：2009 √ ISO 15623：2013 √ ISO 17361：2017 √ ISO 15622：2018 √ SAE J2400—2014 √ SAE J2399—2021 √ 《智能运输系统自适应巡航控制系统性能要求与检测方法》(GB/T 20608—2006) √ 《智能运输系统车道偏离报警系统性能要求与检测方法》 (GB/T 26773—2011) √ 《乘用车自动紧急制动系统(AEBS)性能要求及试验方法》(GB/T 39901—2021) √ NHTSA[262] √ √ √ √ √ Euro-NCAP[263] √ √ √ √ A-NCAP[264] √ √ √ √ √ C-NCAP[265] √
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表 17 自动驾驶测试标准包含的评价指标
Table 17. Evaluation indicators included in autonomous driving test standards
标准 评价指标 伤害的严重性 暴露于危险中的可能性 危险的可控性 风险可接受性 警报准确性 系统报警重复性 警报可接受性 碰撞警报及时性 安全等级独立性 目标识别准确性 防撞击性 探测区域有效性 车道辅助系统的避让性 抗翻滚性 ISO 15623:2013 √ √ √ √ √ ISO 17361:2017 √ √ √ ISO 26262:2018 √ √ √ √ ISO 21448:2022 √ √ √ √ SAE J2400—2003[266] √ √ √ √ √ SAE J2980—2018 √ √ √ SAE J2399—2021 √ GB/T 26773—2011 √ √ √ GB/T 34590—2017 √ √ √ √ NHTSA 26555[262] √ √ √ √ √ NHTSA NCAP[267] √ √ √ √ √ √ √ C-NCAP[267] √ √ √ Euro-NCAP[268] √ √ √ J-NCAP[269] √ √
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表 18 毫米波雷达感知干扰分析框架
Table 18. Analysis framework of sensing interferences from millimeter-wave radars
毫米波雷达 物理原理(信号感知/传感器安装方位角) 被测目标物的信号 其他探测物的信号 频率 相位 能量 噪音 非期望的信号 探测方向 传播延迟变化 无信号(部分信号) 高频 信号差异大 反射 折射 混叠 谐波 低信噪比 有效信号比例低 无效信号增长 感知干扰的因果因子 自车/ 传感器 自车 车辆姿态改变 ○ ○ ○ 传感器 装配松动 ○ ○ ○ ○ ○ 传感器失效 ○ ○ ○ ○ ○ ○ ○ 前表面 黏附物 ○ ○ ○ 物理特性改变 ○ ○ ○ 环境 结构化目标物 道路表面 形状 ○ ○ ○ 道路情况 ○ ○ 路面材质 ○ ○ 路边物体 反射 ○ ○ ○ ○ 遮挡 ○ ○ 背景 头顶物体 反射 ○ ○ 遮挡 ○ 背景 空间 空间物体 ○ ○ ○ ○ 空间电磁波和光线 ○ ○ 移动目标物 反射 ○ ○ ○ ○ ○ 遮挡 背景 感知目标物 车道 线条 颜色/材质 形状 脏污/模糊 相对位置 具有高度的结构化物体 颜色/材质 ○ 形状-反射强度大 ○ ○ ○ 形状-反射强度小 ○ 脏污 ○ 相对位置 ○ 道路边缘 道路边缘(平坦) 颜色/材质 ○ 形状 ○ 脏污/模糊 ○ 相对位置 ○ 道路边缘(不均匀) 颜色/材质 ○ 形状 ○ 脏污/模糊 ○ 相对位置 ○ 道路上的障碍物 掉落物 颜色/材质 ○ 形状 ○ 相对位置/运动 ○ 动物 颜色/材质 形状 ○ 相对位置/运动 ○ 临时建筑 颜色/材质 ○ 形状-反射强度大 ○ ○ ○ 形状-反射强度小 ○ 脏污 ○ 相对位置 ○ 移动目标物 其余车辆 颜色/材质 形状-反射强度大 ○ ○ 形状-反射强度小 ○ 脏污 ○ 相对位置 ○ 摩托车 颜色/材质 ○ 形状/大小 ○ 黏附物 ○ 相对位置 ○ 自行车 颜色/材质 ○ 形状/大小 ○ 黏附物 ○ 相对位置 ○ 行人 颜色/材质 ○ 形状/大小 ○ 相对位置 ○
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表 19 自动驾驶缺陷分类
Table 19. Classification of autonomous driving defects
分类依据 名称 含义 示例 生产环节 制造缺陷 制造过程中产品偏离预期设计 传感器功能障碍 设计缺陷 未采纳替代设计规避可预计的风险 自动驾驶软件设计忽略了某些对人身财产安全具有重要影响的因素 警示缺陷 说明或警示不充分带来的不合理危险 某些特定工况下，自动驾驶系统未能向用户发出警示和接管请求 安全风险来源 功能安全缺陷 导致自动驾驶电子电气系统功能异常的安全风险因素 软硬件故障 预期功能安全缺陷 由于自动驾驶功能实现不足而导致安全风险的因素 传感器感知性能不足 信息安全缺陷 可能使自动驾驶系统处于网络攻击威胁的因素 通信协议漏洞
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表 20 针对智能网联汽车信息安全常见的网络攻击模型
Table 20. Common network attack models for information security of connected and automated vehicles
网络攻击模型 目标组件 访问方式 针对OBD设备的攻击 OBD 物理端口 通过OBD对CAN总线的攻击 CAN 物理端口 远程访问ECU对CAN总线的攻击 CAN 远程访问 通过CAN总线对ECU进行攻击 ECU 物理端口 远程攻击ECU ECU 远程访问 激光雷达欺骗 激光雷达 远程访问 激光雷达干扰 激光雷达 远程访问 雷达欺骗 雷达 远程访问 雷达干扰 雷达 远程访问 GNSS欺骗 GNSS 远程访问 GNSS干扰 GNSS 远程访问 对抗训练的图片干扰 相机 远程访问 网络信息伪造 连接机制 远程访问 网络拒绝服务攻击 连接机制 远程访问
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