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汽车安全与节能学报

汽车安全与节能学报 ›› 2026, Vol. 17 ›› Issue (1): 59-69.DOI: 10.3969/j.issn.1674-8484.2026.01.006

• 汽车安全 • 上一篇    下一篇

面向自动驾驶功能通用检测的安全行车量化评价

马腾1(), 马育林1,*(), 李祎承2, 潘家保1, 许述财3   

  1. 1.安徽工程大学 机械与汽车工程学院，芜湖 241000，中国
    2.江苏大学 汽车工程研究院，镇江 212013，中国
    3.清华大学，智能绿色车辆与交通全国重点实验室，北京 100084，中国
  • 收稿日期:2025-10-17 修回日期:2025-12-30 出版日期:2026-02-28 发布日期:2026-03-19
  • 通讯作者: 马育林，研究员。E-mail：mayulin@mail.ahpu.edu.cn。
  • 作者简介:马腾（1998—），男（汉），河南，硕士研究生。E-mail：2230142102@stu.ahpu.edu.cn。
  • 基金资助:
    安徽省高校杰出青年科研项目(2023AH020015);智能绿色车辆与交通全国重点实验室开放课题(KFY2419)

Quantitative evaluation of automated driving safety oriented general functions detection

MA Teng1(), MA Yulin1,*(), LI Yicheng2, PAN Jiabao1, XU Shucai3   

  1. 1. School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China
    2. Institute of Automotive Engineering, Jiangsu University, Zhenjiang 212013, China
    3. National Key Laboratory of Intelligent Green Vehicles and Transportation, Tsinghua University, Beijing 100084, China
  • Received:2025-10-17 Revised:2025-12-30 Online:2026-02-28 Published:2026-03-19

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摘要：

针对中国智能网联汽车自动驾驶功能场地试验方法及要求，提出了一种面向自动驾驶功能通用检测的安全行车量化评价方法。围绕通用检测项目中与安全行车直接相关的“周边车辆行驶状态识别及响应”和“行人与非机动车识别及响应”，建立涵盖经验驾驶特征提取、车辆逆动力学解耦、F范数矩阵计算的安全行车评价模型；借助PreScan、CarSim、SIMULINK联合搭建安全行车仿真场景，利用设计的安全行车评价模型，计算得到搭载某黑盒自动驾驶系统的2种车型的安全行车量化评价结果。结果表明，采用该文方法测得的车型安全行车得分更符合经验驾驶行为特性；与主流方法对比，2辆被测车辆的综合性能评价准确率分别提高了38.11%和68.57%。

关键词: 自动驾驶通用检测, 安全行车, 经验驾驶, F范数矩阵, 量化评价

Abstract:

A quantitative safety-driving evaluation framework was proposed for autonomous driving functions of intelligent connected vehicles (ICVs) in China. Grounded in a standardized functional assessment protocol, the framework specifically targeted two critical safety-related capabilities, which were “identification and response to the dynamic states of surrounding vehicles”, and “identification and response to pedestrians and non-motorized vehicles”. A comprehensive evaluation model was developed, integrating three methodological components, which were extraction of expert driver behavioral features from naturalistic driving data, vehicle inverse dynamics decoupling to isolate control-relevant motion states, and F-norm-based matrix quantification of trajectory deviation and response timeliness. A high-fidelity co-simulation environment was constructed to enable rigorous validation by integrating PreScan, CarSim and Simulink. The quantitative safety-driving scores were obtained by applying this framework to two ICV platforms equipped with an industry-standard black-box autonomous driving system. The results demonstrate that the proposed method yields scores significantly more consistent with empirically observed expert driving behavior. Relative to conventional evaluation approaches, the framework improves overall performance assessment accuracy by 38.11% and 68.57% for the two test vehicles, respectively.

Key words: automated driving general detection, driving safety, empirical driving, F-norms matrix, quantitative evaluation

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