ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

心理科学进展 ›› 2021, Vol. 29 ›› Issue (12): 2172-2183.doi: 10.3724/SP.J.1042.2021.02172

• 研究前沿 • 上一篇    下一篇

自动驾驶车中的人机信任

高在峰1(), 李文敏1, 梁佳文1, 潘晗希1, 许为2, 沈模卫1   

  1. 1浙江大学心理与行为科学系, 杭州 310007
    2浙江大学心理科学中心, 杭州 310007
  • 收稿日期:2020-12-27 发布日期:2021-10-26
  • 通讯作者: 高在峰 E-mail:zaifengg@zju.edu.cn
  • 基金资助:
    科技创新2030子课题(2018AAA0101605);科技部重点研发计划(2019YFB1600504)

Trust in automated vehicles

GAO Zaifeng1(), LI Wenmin1, LIANG Jiawen1, PAN Hanxi1, XU Wei2, SHEN Mowei1   

  1. 1Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310007, China
    2Center for Psychological Sciences at Zhejiang University, Hangzhou 310007, China
  • Received:2020-12-27 Published:2021-10-26
  • Contact: GAO Zaifeng E-mail:zaifengg@zju.edu.cn

摘要:

自动驾驶是当前智能汽车发展的重要方向。在实现完全自动化驾驶前, 驾驶员和自动驾驶系统共享车辆控制权, 协同完成驾驶任务。在该人-机共驾阶段, 人对自动驾驶系统的信任是影响自动驾驶中人机协同效率与驾驶安全的关键要素; 驾驶员对自动驾驶车辆保持适当的信任水平对驾驶安全至关重要。本研究结合信任的发展阶段与影响因素提出了动态信任框架。该框架将信任发展分为倾向性信任、初始信任、实时信任和事后信任四个发展阶段, 并结合操作者特征(人)、系统特征(自动驾驶车系统)、情境特征(环境)三个关键因素分析不同阶段的核心影响因素以及彼此间的内在关联。根据该框架, 信任校准可从监测矫正、驾驶员训练、优化HMI设计三类途径展开。未来研究应更多关注驾驶员和人机系统设计特征对信任的影响, 考察信任的实时测量和功能特异性, 探讨驾驶员和系统的相互信任机制, 以及提升信任研究的外部效度。

关键词: 信任, 自动驾驶, 动态信任框架, 信任校准, HMI设计

Abstract:

Automated driving (AD) is one of the key directions in the intelligent vehicles field. Before full automated driving, we are at the stage of human-machine cooperative driving: Drivers share the driving control with the automated vehicles. Trust in automated vehicles plays a pivotal role in traffic safety and the efficiency of human-machine collaboration. It is vital for drivers to keep an appropriate trust level to avoid accidents. We proposed a dynamic trust framework to elaborate the development of trust and the underlying factors affecting trust. The dynamic trust framework divides the development of trust into four stages: dispositional, initial, ongoing, and post-task trust. Based on the operator characteristics (human), system characteristics (automated driving system), and situation characteristics (environment), the framework identifies potential key factors at each stage and the relation between them. According to the framework, trust calibration can be improved from three approaches: trust monitoring, driver training, and optimizing HMI design. Future research should pay attention to the following four perspectives: the influence of driver and HMI characteristics on trust, the real-time measurement and functional specificity of trust, the mutual trust mechanism between drivers and AD systems, and ways in improving the external validity of trust studies.

Key words: trust, automated driving, dynamic trust framework, trust calibration, HMI design

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