智能系统可靠性仿真测试与验证技术：前沿进展与挑战

doi:10.16182/j.issn1004731x.joss.25-0554

系统仿真学报 ›› 2025, Vol. 37 ›› Issue (7): 1583-1606.doi: 10.16182/j.issn1004731x.joss.25-0554

• 特约综述 •

智能系统可靠性仿真测试与验证技术：前沿进展与挑战

王自力¹^,², 高鋆添¹^,², 杨德真¹^,², 刘烨炀¹^,², 任羿¹^,²

^1.可靠性与环境工程技术全国重点实验室，北京 100191
^2.北京航空航天大学可靠性与系统工程学院，北京 100191

收稿日期:2025-06-13 修回日期:2025-06-30 出版日期:2025-07-18 发布日期:2025-07-30
通讯作者: 杨德真
第一作者简介:王自力 1964年生，北京航空航天大学教授、博士生导师。现任可靠性与环境工程技术全国重点实验室主任，兼任中国仿真学会理事长、系统仿真学报主编及多个全国或省部级重点实验室学术委员会主任等职。长期从事可靠性系统工程理论研究与重大工程实践，形成可靠性综合集成理论，研发国际先进的性能与可靠性数字化协同设计集成平台，成果已在国内多个领域广泛应用，为大幅提升产品质量与可靠性水平奠定了重要基础。相关成果发表论文100余篇，主编出版学术专著7部；获国家科技进步二等奖2项，省部级科技创新团队奖1项、科技进步一等奖4项，率团队集体荣获国家科技进步特等奖2项。
王自力(1964-)，男，研究员，硕士，研究方向为可靠性系统工程。

Reliability Simulation Testing and Verification Technologies for Intelligent Systems: Frontiers, Progress, and Challenges

Wang Zili¹^,², Gao Yuntian¹^,², Yang Dezhen¹^,², Liu Yeyang¹^,², Ren Yi¹^,²

^1.National Key Laboratory of Reliability and Environmental Engineering, Beijing 100191, China
^2.School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Received:2025-06-13 Revised:2025-06-30 Online:2025-07-18 Published:2025-07-30
Contact: Yang Dezhen

摘要/Abstract

摘要：

智能系统在交通运输、能源水利、智慧医疗、航空航天等领域有着广泛的应用前景，其可靠性直接关乎公共安全与社会稳定，必须经过科学充分的验证。深入探讨了智能系统可靠性仿真验证技术的现状，定义了智能系统可靠性，并指出了智能系统在任务场景建模、特性建模仿真、评估验证、仿真平台等方面所面临的诸多挑战。提出了未来的发展需求，为更可信、精准、高效的智能系统可靠性仿真测试与验证技术研究提供指导，推动智能系统的高质量发展。

关键词: 智能系统, 可靠性, 任务场景, 可靠性仿真测试, 可靠性评估, 可靠性验证

Abstract:

Intelligent systems are extensively deployed in domains such as transportation, energy and water resources, smart healthcare, and aerospace, where their reliability is directly linked to public safety and social stability, thus requiring thorough and scientifically rigorous verification. This article conducted an in-depth exploration of the current state of reliability simulation and verification techniques for intelligent systems. It defined the concept of reliability specific to intelligent systems and identified key challenges they face in areas including mission scenario modeling, characteristic modeling and simulation, evaluation and verification, and simulation platforms. Future development requirements were proposed to guide research toward more trustworthy, precise, and efficient reliability simulation and verification technologies for intelligent systems, thereby facilitating their high-quality development.

Key words: intelligent system, reliability, mission scenario, reliability simulation testing, reliability evaluation, reliability verification

中图分类号:

TP18

王自力,高鋆添,杨德真等 . 智能系统可靠性仿真测试与验证技术：前沿进展与挑战[J]. 系统仿真学报, 2025, 37(7): 1583-1606.

Wang Zili,Gao Yuntian,Yang Dezhen,et al . Reliability Simulation Testing and Verification Technologies for Intelligent Systems: Frontiers, Progress, and Challenges[J]. Journal of System Simulation, 2025, 37(7): 1583-1606.

图/表 13

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表3

智能系统可靠性仿真平台核心功能对比

平台	多模态数据融合	动态场景建模	故障注入与传播分析	实时性与可扩展性	开放性与可定制化
Apollo	高精度地图与传感器融合(毫米级精度)，依赖真实硬件标定	基于规则生成交通流，极端场景依赖人工设计	支持传感器信号丢失、算法逻辑错误注入，对抗攻击漏洞覆盖不足	支持分布式部署，高精度仿真依赖本地GPU算力	开源代码，可自定义模块，架构复杂、调试门槛高
CARLA(Uber)	物理级传感器模型(LiDAR/摄像头)，数据保真度受虚幻引擎限制	动态生成天气、光照、交通流，长尾场景需脚本扩展	通过Python API注入传感器噪声、通信延迟，缺乏系统级故障传播量化	分布式并行仿真(多任务同步)，大规模场景渲染延迟显著	开源虚幻引擎插件，Python API调用灵活
Autoware(开源社区)	模块化传感器接口(ROS/ROS2)，需第三方工具提升融合精度	依赖外部场景工具(如CARLA)，具备多智能体交互建模能力	结合AVP工具包实现局部故障诊断，无系统性故障链分析	单机运行扩展性受限，模块化架构支持功能扩展	ROS节点化设计，生态丰富，硬件性能要求高
Udacity Simulator	基础传感器模拟(低保真)，无异构数据同步机制	预设简单场景(无动态交互)，无极端条件支持	不支持主动故障注入	单线程运行，实时性差，仅支持小规模测试	封闭式 API，图形界面操作，无底层修改权限
TAD Sim	数字孪生与AI交通流仿真，可进行物理级传感器噪声注入	自动生成 “鬼探头”“团雾”等极端场景，有拟人化背景车模型	全链路故障注入(传感器-计算-执行)，可量化故障传播概率(如海况影响)	云端高并发(千级并行)，支持硬件在环(HIL)实时测试	核心功能私有(如云端调度)，部分模块支持API接入

表3

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分级	名称	自动驾驶车辆动作控制	无人机飞行任务执行
0	应急辅助	驾驶员	操作人员
1	部分驾驶辅助	驾驶员与系统	系统+操作人员
2	结合驾驶辅助	系统	系统
3	有条件自动驾驶	系统	系统
4	高度自动驾驶	系统	系统
5	完全自主	系统	系统

指标	内涵	主要应用领域	侧重点
准确率	正确预测与总预测次数的比率	金融领域的信用评分模型；医疗领域的疾病判断模型	评价模型预测能力^[54]
精确率	正确阳性预测与预测阳性的比例	医疗领域的疾病判断模型	在分类任务中评估模型的阳性预测性能
召回率	正确阳性预测与实际阳性的比例	医疗领域的疾病判断模型	识别出所有阳性样本的能力
F1值	精确度与召回率的调和平均值	金融领域，信用评分和欺诈检测模型；舆情领域，文本分析模型的分类效果	综合考虑误报与漏报，提供对模型分类性能的整体评估维度^[55]
鲁棒性	评估在复杂且动态环境下是否维持稳定性能	在自动驾驶领域，需确保在复杂路况和不同天气下仍能稳定运行，避免安全风险	面对输入数据分布变化、噪声干扰或其他外部环境因素时，能够持续输出一致且准确的预测结果^[54]

智能系统可靠性仿真测试与验证技术：前沿进展与挑战

Reliability Simulation Testing and Verification Technologies for Intelligent Systems: Frontiers, Progress, and Challenges

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