CO-Mode with AV-CRM: A novel paradigm towards human–machine collaboration in intelligent vehicle safety

With the continuous advancement in vehicle intelligence, enhancing safety has emerged as a key priority in intelligent vehicle research and development. Intelligent vehicles are currently limited in supporting autonomous or human-driven modes. This limitation becomes apparent in complex driving scenarios, where vehicle risk response capabilities are inadequate. This paper suggests that shifting from a single decision maker to a human–machine collaboration approach is a potential solution. However, current research on human–machine collaboration in intelligent vehicles primarily focuses on intelligent systems that assist the driver, rather than treating the driver and the system as equals. This approach overlooks the role of the driver in helping the system, lacks effective communication, and diminishes the sense of collaborative cooperation, all of which hinder the promotion of efficient, safe, and stable driving. Inspired by the aviation approach to risk management through Crew Resource Management (CRM), this study introduces the Collaborative Operation Mode (CO-Mode) for intelligent vehicles. Based on CO-Mode’s requirements for human–machine collaborative perception, decision making, and control, the Autonomous Vehicle Collaborative Resource Management (AV-CRM) is proposed. In addition, challenges and future perspectives are explored by analyzing the abilities and limitations of relevant technologies. The proposed AV-CRM redefines the relationship between drivers and intelligent systems, offering new insights into the safety of intelligent vehicles and their technological evolution.

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Publication Details

Journal
Journal of Safety Science and Resilience
Published
2025-06-17
DOI
https://doi.org/10.1016/j.jnlssr.2025.04.002
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

CO-Mode with AV-CRM: A novel paradigm towards human–machine collaboration in intelligent vehicle safety

Yaoguang Cao, Shichun Yang, Zhaoxia Peng, Jiayi Lu et al.
Journal of Safety Science and Resilience
Human-Automation Interaction and Safety
article

CO-Mode with AV-CRM: A novel paradigm towards human–machine collaboration in intelligent vehicle safety

Yaoguang Cao, Shichun Yang, Zhaoxia Peng, Jiayi Lu, Bin Sun, Zhaowen Pang, Boao Zhang
article en

Abstract

With the continuous advancement in vehicle intelligence, enhancing safety has emerged as a key priority in intelligent vehicle research and development. Intelligent vehicles are currently limited in supporting autonomous or human-driven modes. This limitation becomes apparent in complex driving scenarios, where vehicle risk response capabilities are inadequate. This paper suggests that shifting from a single decision maker to a human–machine collaboration approach is a potential solution. However, current research on human–machine collaboration in intelligent vehicles primarily focuses on intelligent systems that assist the driver, rather than treating the driver and the system as equals. This approach overlooks the role of the driver in helping the system, lacks effective communication, and diminishes the sense of collaborative cooperation, all of which hinder the promotion of efficient, safe, and stable driving. Inspired by the aviation approach to risk management through Crew Resource Management (CRM), this study introduces the Collaborative Operation Mode (CO-Mode) for intelligent vehicles. Based on CO-Mode’s requirements for human–machine collaborative perception, decision making, and control, the Autonomous Vehicle Collaborative Resource Management (AV-CRM) is proposed. In addition, challenges and future perspectives are explored by analyzing the abilities and limitations of relevant technologies. The proposed AV-CRM redefines the relationship between drivers and intelligent systems, offering new insights into the safety of intelligent vehicles and their technological evolution.

Journal of Safety Science and ResilienceVol. 7(1)
Beihang University (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 85%
Human-Automation Interaction and Safety
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