In-vehicle displays for driving automation: a scoping review of display design and evaluation using driver gaze measures

被引:1
作者
Kanaan, Dina [1 ]
Powell, Mattea [1 ]
Lu, Michael [2 ]
Donmez, Birsen [1 ]
机构
[1] Univ Toronto, Dept Mech & Ind Engn, 5 Kings Coll Rd, Toronto, ON M5S 3G8, Canada
[2] McMaster Univ, Dept Comp & Software, Hamilton, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Driving automation; displays; HMI; gaze; eye tracking; knowledge synthesis; SITUATION AWARENESS; TAKEOVER REQUESTS; TIME; DISTRACTION; PERFORMANCE; IMPACT;
D O I
10.1080/01441647.2024.2336921
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Recent research has extensively examined in-vehicle display designs for supporting the operation of driving automation. As automation relieves drivers from various driving tasks including vehicle control (e.g. steering, accelerating, and braking), driving performance measures (e.g. speed, lane deviations) may not be informative indicators for evaluating the effectiveness of in-vehicle displays. Gaze-based measures are a better alternative given their link to driver visual attention, an indication of driver engagement. A scoping review was conducted to review the literature on the design of displays for supporting the operation of driving automation and the evaluation of these displays using gaze-based measures. Forty-three articles were included in the review. Most of the studies investigated visual (and mixed visual-auditory) displays that provide alerts to the driver for when to intervene automation classified as Level 3. The adopted gaze measures mostly relied on static areas of interest (AOIs), with fewer studies looking at more fine-grained, context dependent AOIs. The paper summarises the findings of the review, including research trends and gaps, as well as recommendations for future research.
引用
收藏
页码:858 / 888
页数:31
相关论文
共 92 条
[1]   Toward Adaptive Trust Calibration for Level 2 Driving Automation [J].
Akash, Kumar ;
Jain, Neera ;
Misu, Teruhisa .
PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, ICMI 2020, 2020, :538-547
[2]  
[Anonymous], 2020, Collision Between a Sport Utility Vehicle Operating With Partial Driving Automation and a Crash Attenuator, Mountain View, California
[3]  
Arksey H., 2005, INT J SOC RES METHOD, V8, P19, DOI [10.1080/1364557032000119616, DOI 10.1080/1364557032000119616]
[4]  
Bigelow P., 2023, AUTOMOTIVE NEWS
[5]   Manipulating music to communicate automation reliability in conditionally automated driving: A driving simulator study [J].
Chen, Kuan-Ting ;
Chen, Huei-Yen Winnie .
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 2021, 145
[6]   The impact of auditory continual feedback on take-overs in Level 3 automated vehicles [J].
Cohen-Lazry, Guy ;
Borowsky, Avinoam ;
Oron-Gilad, Tal .
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2020, 75 :145-159
[7]   Context-Adaptive Availability Notifications for an SAE Level 3 Automation [J].
Danner, Simon ;
Feierle, Alexander ;
Manger, Carina ;
Bengler, Klaus .
MULTIMODAL TECHNOLOGIES AND INTERACTION, 2021, 5 (04)
[8]   Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence [J].
de Winter, Joost C. F. ;
Happee, Riender ;
Martens, Marieke H. ;
Stanton, Neville A. .
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2014, 27 :196-217
[9]   Takeover request (TOR) effects during different automated vehicle failures [J].
DeGuzman, Chelsea A. ;
Kanaan, Dina ;
Hopkins, Samantha A. ;
Donmez, Birsen .
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2021,
[10]   On the forces of driver distraction: Explainable predictions for the visual demand of in-vehicle touchscreen interactions [J].
Ebel, Patrick ;
Lingenfelder, Christoph ;
Vogelsang, Andreas .
ACCIDENT ANALYSIS AND PREVENTION, 2023, 183