Research on dashboard interface interaction design based on visual selective attention

doi:10.11996/JG.j.2095-302X.2026040927

Abstract

Abstract:

In intelligent connected vehicle driving scenarios, the increasing complexity of in-vehicle instrument cluster information has led to heightened visual interference and greater cognitive workload for drivers. Based on the theory of selective visual attention, this study investigated the effects of information weighting, spatial layout, and color attributes on drivers’ attentional allocation and information acquisition efficiency, and developed corresponding design strategies aimed at improving interface readability and cognitive efficiency. This research was organized around three dimensions: “information weight-layout positioning-color characteristics.” First, the Analytic Hierarchy Process (AHP) was employed to construct a hierarchical model of in-vehicle instrument cluster information, allowing the extraction and quantification of the weight relationships among key information elements. Second, eye-tracking experiments were conducted with center-aligned and dumbbell-shaped layouts as the primary experimental conditions to analyze differences in cognitive efficiency for high-weight information, including vehicle speed, battery level and remaining range, power output, and gear status, under different spatial configurations. Third, a k-means clustering algorithm was applied to extract visual characteristics such as primary color, background color, font color, and auxiliary color, thereby summarizing the color design patterns of in-vehicle instrument cluster interfaces. Finally, based on the research findings, interface design strategies were proposed, and a comprehensive evaluation was conducted through system usability assessment, eye-tracking analysis, and user preference testing to validate the design solutions and identify the optimal scheme. The results indicated that essential driving information had the highest overall weight within the information system. Among these, vehicle speed, battery level and remaining range, power output, and gear status were identified as the most critical information elements receiving the greatest driver attention during driving tasks. When high-weight information was arranged in the visual center and its surrounding regions, both information search efficiency and recognition efficiency were significantly improved. In terms of color design, blue and blue-green color schemes are the mainstream visual styles for current instrument cluster interfaces, effectively balancing readability and visual comfort. The evaluation results further demonstrated that the proposed interface solutions exhibited good system usability. In particular, the center-aligned layout combined with a blue-green color scheme achieved superior performance in both cognitive efficiency and user preference. In conclusion, the information hierarchy, layout optimization, and color design strategies constructed based on the theory of selective visual attention effectively optimized the information structure and visual presentation of in-vehicle instrument clusters. These strategies significantly enhanced information recognition efficiency and interface readability, providing both theoretical foundations and practical guidance for the design and development of intelligent connected vehicle instrument cluster interfaces.

Key words: visual selective attention, vehicle dashboard, interface interaction design, driving safety, human-computer interaction

CLC Number:

TP391
U463.7

YU Xianyuan, ZHU Zhaohua, WU Jingtong, TANG Tiantian, WANG Wenyu. Research on dashboard interface interaction design based on visual selective attention[J]. Journal of Graphics, 2026, 47(4): 927-939.

Figures/Tables 30

References 24

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准则层	权重值	方案层	初始权重值	综合权重值
行车基本信息(B1)	0.528 6	车速信息(C1)	0.346 0	0.182 9
		功率信息(C2)	0.153 9	0.081 4
		档位信息(C3)	0.093 8	0.049 6
		电量及续航里程信息(C4)	0.275 1	0.145 4
		日期与时间信息(C5)	0.024 6	0.013 0
		温度信息(C6)	0.031 4	0.016 6
		驾驶模式信息(C7)	0.075 4	0.039 9
车辆状态信息(B2)	0.218 4	里程信息(C8)	0.054 4	0.011 9
		故障与指示信息(C9)	0.491 5	0.107 3
		胎压监测信息(C10)	0.328 3	0.071 7
		能量记录信息(C11)	0.125 8	0.027 5
驾驶辅助信息(B3)	0.205 6	地图导航信息(C12)	0.519 7	0.106 9
		自适应巡航提示信息(C13)	0.108 5	0.022 3
		车道偏离预警信息(C14)	0.180 3	0.037 1
		前向碰撞预警信息(C15)	0.191 5	0.039 4
娱乐与通讯信息(B4)	0.047 4	音乐播放信息(C16)	0.310 2	0.014 7
		电台播放信息(C17)	0.319 0	0.015 1
		通讯信息(C18)	0.370 8	0.017 6

准则层	权重值	方案层	初始权重值	综合权重值
行车基本信息(B1)	0.528 6	车速信息(C1)	0.346 0	0.182 9
		功率信息(C2)	0.153 9	0.081 4
		档位信息(C3)	0.093 8	0.049 6
		电量及续航里程信息(C4)	0.275 1	0.145 4
		日期与时间信息(C5)	0.024 6	0.013 0
		温度信息(C6)	0.031 4	0.016 6
		驾驶模式信息(C7)	0.075 4	0.039 9
车辆状态信息(B2)	0.218 4	里程信息(C8)	0.054 4	0.011 9
		故障与指示信息(C9)	0.491 5	0.107 3
		胎压监测信息(C10)	0.328 3	0.071 7
		能量记录信息(C11)	0.125 8	0.027 5
驾驶辅助信息(B3)	0.205 6	地图导航信息(C12)	0.519 7	0.106 9
		自适应巡航提示信息(C13)	0.108 5	0.022 3
		车道偏离预警信息(C14)	0.180 3	0.037 1
		前向碰撞预警信息(C15)	0.191 5	0.039 4
娱乐与通讯信息(B4)	0.047 4	音乐播放信息(C16)	0.310 2	0.014 7
		电台播放信息(C17)	0.319 0	0.015 1
		通讯信息(C18)	0.370 8	0.017 6

测量指标	位置(I)	位置(J)	平均值差值(I-J)	标准误差	显著性^b	差值的 95%置信区间^b
测量指标	位置(I)	位置(J)	平均值差值(I-J)	标准误差	显著性^b	下限	上限
首次注视时间	d₁	d₂	0.328*	0.087	0.002	0.108	0.548
	d₁	d₃	0.109	0.098	0.831	-0.141	0.359
	d₂	d₁	-0.328*	0.087	0.002	-0.548	-0.108
	d₂	d₃	-0.219*	0.068	0.010	-0.393	-0.045
	d₃	d₁	-0.109	0.087	0.831	-0.359	0.141
	d₃	d₂	0.219*	0.068	0.010	0.045	0.393
总反应时	d₁	d₂	0.438*	0.073	0.000	0.251	0.624
	d₁	d₃	0.008	0.099	1.000	-0.243	0.260
	d₂	d₁	-0.438*	0.073	0.000	-0.624	-0.251
	d₂	d₃	-0.429*	0.096	0.000	-0.673	-0.185
	d₃	d₁	-0.008	0.099	1.000	-0.260	0.243
	d₃	d₂	0.429*	0.096	0.000	0.185	0.673

测量指标	位置(I)	位置(J)	平均值差值(I-J)	标准误差	显著性^b	差值的 95%置信区间^b
测量指标	位置(I)	位置(J)	平均值差值(I-J)	标准误差	显著性^b	下限	上限
首次注视时间	d₁	d₂	0.328*	0.087	0.002	0.108	0.548
	d₁	d₃	0.109	0.098	0.831	-0.141	0.359
	d₂	d₁	-0.328*	0.087	0.002	-0.548	-0.108
	d₂	d₃	-0.219*	0.068	0.010	-0.393	-0.045
	d₃	d₁	-0.109	0.087	0.831	-0.359	0.141
	d₃	d₂	0.219*	0.068	0.010	0.045	0.393
总反应时	d₁	d₂	0.438*	0.073	0.000	0.251	0.624
	d₁	d₃	0.008	0.099	1.000	-0.243	0.260
	d₂	d₁	-0.438*	0.073	0.000	-0.624	-0.251
	d₂	d₃	-0.429*	0.096	0.000	-0.673	-0.185
	d₃	d₁	-0.008	0.099	1.000	-0.260	0.243
	d₃	d₂	0.429*	0.096	0.000	0.185	0.673

测量指标	位置(I)	位置(J)	平均值差值(I-J)	标准误差	显著性^b	差值的 95%置信区间^b
测量指标	位置(I)	位置(J)	平均值差值(I-J)	标准误差	显著性^b	下限	上限
首次注视时间	d₁	d₂	0.377*	0.112	0.006	0.093	0.662
	d₁	d₃	-0.039	0.129	1.000	-0.365	0.287
	d₂	d₁	-0.377*	0.112	0.006	-0.662	-0.093
	d₂	d₃	-0.416	0.121	0.005	-0.722	-0.111
	d₃	d₁	0.039	0.129	1.000	-0.287	0.365
	d₃	d₂	0.416*	0.121	0.005	-0.111	0.722
总反应时	d₁	d₂	0.446*	0.101	0.000	0.189	0.702
	d₁	d₃	-0.067	0.105	1.000	-0.333	0.200
	d₂	d₁	-0.446*	0.101	0.000	-0.702	-0.189
	d₂	d₃	-0.513*	0.101	0.000	-0.768	-0.257
	d₃	d₁	0.067	0.105	1.000	-0.200	0.333
	d₃	d₂	0.513*	0.101	0.000	0.257	0.768