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A Safety Assessment Method for AI-Powered Automated Driving Systems
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Zhen CHEN1, Jingtai LI2, Huang GUO3, Xiaoqing XU1
Chinese Journal of Automotive Engineering | 2025, 15(4) : 457 - 467
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Chinese Journal of Automotive Engineering | 2025, 15(4): 457-467
• Safety Technology Section/ Editor-in-Chief：CAO Libo •
A Safety Assessment Method for AI-Powered Automated Driving Systems
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Zhen CHEN1, Jingtai LI2, Huang GUO3, Xiaoqing XU1
Affiliations
  • 1 Beijing Dishi Data Technology Co.，Ltd.，Beijing 100176，China
  • 2 Equipment Industry Development Center，Ministry of Industry and Information Technology，Beijing 100846，China
  • 3 Beijing Saimo Technology Co.，Ltd.，Beijing 100080，China
Published: 2025-07-20 doi: 10.3969/j.issn.2095‒1469.2025.04.03
Outline
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The rapid development of connected and intelligent vehicles is accelerating the exploration and commercialization of artificial intelligence (AI) technologies. Yet the broader and deeper application of AI in automated driving also brings increasingly prominent safety risks. Thus, developing safety testing and assessment methods for AI-applied automated driving systems is crucial for balancing technological innovation with safety concerns. From a system-safety perspective, this paper proposes a safety assessment method covering three stages: design and development, testing and evaluation, and deployment and operation. The method integrates the life cycle of AI system, safety requirements, verification and validation methods, and continuous risk assessment and safety analysis. Furthermore, the measures for development, design, testing, and optimization to ensure system safety are proposed, providing a reference for future testing and safety assessment of AI-based automated driving systems.

artificial intelligence  /  automated driving  /  intelligent and connected vehicle  /  testing methods  /  safety assessment
Zhen CHEN, Jingtai LI, Huang GUO, Xiaoqing XU. A Safety Assessment Method for AI-Powered Automated Driving Systems[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (4) : 457 -467 . DOI: 10.3969/j.issn.2095‒1469.2025.04.03
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Year 2025 volume 15 Issue 4
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Article Info
doi: 10.3969/j.issn.2095‒1469.2025.04.03
  • Receive Date：2024-05-29
  • Online Date：2025-09-10
  • Published：2025-07-20
Article Data
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History
  • Received：2024-05-29
  • Revised：2024-07-16
Funding
Affiliations
    1 Beijing Dishi Data Technology Co.，Ltd.，Beijing 100176，China
    2 Equipment Industry Development Center，Ministry of Industry and Information Technology，Beijing 100846，China
    3 Beijing Saimo Technology Co.，Ltd.，Beijing 100080，China
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https://castjournals.cast.org.cn/joweb/qcgcxb/EN/10.3969/j.issn.2095‒1469.2025.04.03
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