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The Safety Certification Challenges of AEB Under ADAS System: Perception Limitations, Verification Paradigm, and Human-Machine Collaboration
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Kun Cheng, Zhe Li, Jintao Lu, Hanyue Mo, Ziwen Kuang, Jianwen Ye
Automotive Digest | 2026, (2) : 12 - 24
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Automotive Digest | 2026, (2): 12-24
• Special Topic on Autonomous Driving Technology •
The Safety Certification Challenges of AEB Under ADAS System: Perception Limitations, Verification Paradigm, and Human-Machine Collaboration
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Kun Cheng, Zhe Li, Jintao Lu, Hanyue Mo, Ziwen Kuang, Jianwen Ye
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  • Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Hangzhou 310018
Published: 2026-02-05 doi: 10.19822/j.cnki.1671-6329.20250190
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Autonomous Emergency Braking (AEB), as a core safety function of Advanced Driver-Assistance Systems (ADAS), faces 3 major certification challenges: perception limitations, verification complexity, and human-machine collaboration. This paper systematically analyzes the technical architecture of AEB, including multi-sensor fusion for robust environmental perception, conservative decision-making logic to ensure intervention safety, and Operational Design Domain (ODD) definition for user expectation management. In terms of certification frameworks, the study explores the dual-pillar model based on ISO 26262 Functional Safety (FS) and ISO 21448 Safety of the Intended Functionality (SOTIF), highlighting the shift from regulatory testing to scenario-library-driven virtual validation. For human-machine collaboration, it examines mechanisms for establishing calibrated driver trust through transparent interaction design, and prospects the potential of neuro-symbolic AI in enhancing interpretability and localized innovation for China's complex traffic scenarios. This research provides a systematic reference for the industrial safe deployment of AEB systems.

Autonomous Emergency Braking (AEB)  /  Advanced Driver-Assistance Systems (ADAS)  /  Safety certification  /  Safety of the Intended Functionality (SOTIF)  /  Human-machine collaboration  /  Sensor fusion
程坤, 李哲, 卢劲涛, 莫涵越, 邝子文, 叶见文. ADAS体系下AEB的安全认证挑战:感知局限、验证范式与人机协同. 汽车文摘, 2026 , (2) : 12 -24 . DOI: 10.19822/j.cnki.1671-6329.20250190
Kun Cheng, Zhe Li, Jintao Lu, Hanyue Mo, Ziwen Kuang, Jianwen Ye. The Safety Certification Challenges of AEB Under ADAS System: Perception Limitations, Verification Paradigm, and Human-Machine Collaboration[J]. Automotive Digest, 2026 , (2) : 12 -24 . DOI: 10.19822/j.cnki.1671-6329.20250190
Year 2026 volume Issue 2
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doi: 10.19822/j.cnki.1671-6329.20250190
  • Online Date:2026-09-23
  • Published:2026-02-05
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    Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Hangzhou 310018
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表12种不同金属材料的力学参数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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