User experience evaluation of human machine interface in automotive autonomous driving takeover system

doi:10.11996/JG.j.2095-302X.2025020459

Abstract

Abstract:

To address the user experience evaluation problem of the human machine interface in autonomous driving takeover systems, a study was conducted on the evaluation index system and validation research of the autonomous driving takeover system. Firstly, based on in-depth interview and grounded theory, interviews were conducted with typical target individuals to establish an evaluation index system for the human-machine interface experience of autonomous driving takeover systems. The index system was composed of 18 indicators in four dimensions, namely safety experience, functional experience, efficiency experience and emotional experience. An evaluation scale for human-machine interface experience of autonomous driving takeover system was subsequently completed. Then, using the structural equation model method, 241 valid questionnaires were collected to construct a structural equation model of human machine interface experience evaluation of autonomous driving takeover system, and the evaluation dimension and index weights were determined. Finally, the design strategy for the human machine interface of the autonomous driving takeover system was proposed. The effectiveness of the evaluation system was verified through a design case study of the human machine interface of an L3 level autonomous driving takeover system. The research results provided relevant references and support for the field of interface design and experience evaluation of automotive autonomous driving system.

Key words: automotive autonomous driving takeover system, human machine interface, user experience evaluation, grounded theory, structural equation model

CLC Number:

TP391
U463

WU Lei, SHENG Qinqin, ZHAO Ruisi. User experience evaluation of human machine interface in automotive autonomous driving takeover system[J]. Journal of Graphics, 2025, 46(2): 459-468.

Figures/Tables 15

References 25

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编号	模型	CMIN/DF	CFI	NFI	RMSEA	模型比较	Δx2	Δdf
1	原模型	1.642	0.950	0.882	0.052	-	-	-
2	三因子模型一	1.765	0.907	0.873	0.056	2vs1	17.881***	1
3	三因子模型二	1.864	0.899	0.865	0.600	3vs1	30.909***	1
4	三因子模型三	1.933	0.897	0.860	0.620	4vs1	39.989***	1
5	二因子模型	2.041	0.890	0.850	0.660	5vs1	58.415***	3
6	单因子模型	2.143	0.883	0.842	0.690	6vs1	74.087***	4

编号	模型	CMIN/DF	CFI	NFI	RMSEA	模型比较	Δx2	Δdf
1	原模型	1.642	0.950	0.882	0.052	-	-	-
2	三因子模型一	1.765	0.907	0.873	0.056	2vs1	17.881***	1
3	三因子模型二	1.864	0.899	0.865	0.600	3vs1	30.909***	1
4	三因子模型三	1.933	0.897	0.860	0.620	4vs1	39.989***	1
5	二因子模型	2.041	0.890	0.850	0.660	5vs1	58.415***	3
6	单因子模型	2.143	0.883	0.842	0.690	6vs1	74.087***	4

拟合指标	推荐范围	测量值	是否符合标准
CMIN/DF	<3.00	1.642	<3.00可接受，良好
RMR	<0.05	0.023	<0.05可接受，良好
RMSEA	<0.05	0.052	<0.08可接受，良好
GFI	>0.90	0.914	>0.90可接受，良好
AGFI	>0.90	0.888	>0.80可接受，良好
NFI	>0.90	0.882	>0.80可接受，良好
CFI	>0.90	0.950	>0.90可接受，良好
IFI	>0.90	0.950	>0.90可接受，良好

拟合指标	推荐范围	测量值	是否符合标准
CMIN/DF	<3.00	1.642	<3.00可接受，良好
RMR	<0.05	0.023	<0.05可接受，良好
RMSEA	<0.05	0.052	<0.08可接受，良好
GFI	>0.90	0.914	>0.90可接受，良好
AGFI	>0.90	0.888	>0.80可接受，良好
NFI	>0.90	0.882	>0.80可接受，良好
CFI	>0.90	0.950	>0.90可接受，良好
IFI	>0.90	0.950	>0.90可接受，良好

作用路径	非标准化负荷量	S.E.	C.R.(t-value)	P	标准化负荷量	结论
功能体验<---用户体验评价	1.000	-	-	-	0.925	支持
效能体验<---用户体验评价	1.020	0.136	7.504	*	0.882	支持
安全体验<---用户体验评价	0.926	0.115	8.039	*	0.950	支持
感性体验<---用户体验评价	0.929	0.120	7.738	*	0.907	支持