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The Review of Human–Machine Collaborative Intelligent Interaction With Driver Cognition in the Loop

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  • Qianwen Fu
  • Lijun Zhang
  • Yiqian Xu
  • Fang You

Abstract

Background: The traditional human–vehicle relationship and the challenges posed by complex driving scenarios have led to situations where drivers experience ‘Out of the Loop’ (OOTL) cognition, resulting in inefficient human–vehicle communication and a threat to driving safety. Purpose: The cognitive state of drivers in an interactive environment significantly influences the level of collaborative efficiency. This study investigates the interactive logic and interaction modes of intelligent systems that promote driver cognition in the loop, aiming to improve driving experience and safety. Methods: This paper addresses the issue of driver cognition in the loop within human–vehicle collaboration through knowledge graphs and literature reviews to elucidate the evolution of human–vehicle relationships and analyse key elements of collaboration. By examining the characteristics of cognitive behaviours during the driver's perception, understanding, prediction, decision‐making and action phases, it summarizes the impact mechanisms and solutions of the driver's perception, understanding and prediction in the loop, as well as decision‐making and action in the loop on driving tasks. Finally, it provides design strategies and evaluation methods for the development of human–vehicle intelligent systems and intelligent cockpit interaction design.

Suggested Citation

  • Qianwen Fu & Lijun Zhang & Yiqian Xu & Fang You, 2025. "The Review of Human–Machine Collaborative Intelligent Interaction With Driver Cognition in the Loop," Systems Research and Behavioral Science, Wiley Blackwell, vol. 42(4), pages 954-977, July.
  • Handle: RePEc:bla:srbeha:v:42:y:2025:i:4:p:954-977
    DOI: 10.1002/sres.3141
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