Distractive or Supportive -- How Warnings in the Head-up Display Affect Drivers' Gaze and Driving Behavior

Winkler, Susann; Kazazi, Juela; Vollrath, Mark

doi:10.1109/itsc.2015.172

Cited by 19 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This first is that the research question concerns a particular area of fixation. For example, fixations on specific road signs [38] or hazard indicators can be an important criterion for determining whether a driver will be aware of a hazard the sign references; fixations on specific panels are therefore an essential standard to evaluate the performance of pilots [3], [48]. Another necessary condition of the stimuli-generated method is that stimuli are easy to separate.…”

Section: B Stimulus-generated Aoismentioning

confidence: 99%

A Survey of Eye Tracking in Automobile and Aviation Studies: Implications for Eye-Tracking Studies in Marine Operations

Mao

Hildre

et al. 2021

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

In the last decade researchers have increasingly considered eye tracking of the operators of cars and airplanes as a means to address human error and evaluate operational effectiveness. This article presents a systematic survey of recently published papers about this approach in service to the question as to whether eye tracking can be used to address operational safety in marine operations. The surveyed papers are selected systematically and were categorized according to several defined characteristics. Eye tracking depends on defining operators' areas of interest (AOIs) and measuring operators focus on them over time. We identified the method of defining AOIs as a key distinction between studies; the papers fell into four categories, depending on whether researchers relied on an expert, based it on the stimulus itself, or used an attention map or a clustering algorithm to define the AOIs they used. The article also summarizes and analyzes the design and procedure of the eye-tracking experiments in the papers. Based on the features of marine operation, instruction on AOI definition in different scenarios is extracted; guidelines on experimental design and procedure selection are provided. In the article's conclusion we apply the results to a case study of a heavy-lifting operation to demonstrate the effectiveness of eye-tracking in marine operations. Index Terms-Area of interest (AOI) definition method, automobile and aviation, eye tracking, knowledge transfer, marine operation. I. INTRODUCTION E YE tracking is a sensor technology that captures human eye behavior. It has been widely used to gain insight about automobile operation [1], [2], aviation [3], [4], web searching [5], reading [6], drawing [7], medical surgery [8], energy [9], mining [10], offshore [11], and the effect of depictions of healthy and unhealthy foods on children [12]. Among these, automobile and aviation are the two frequently studied fields with a wide range of research applications. Studies in these areas have focused either directly on safety or mental testing of drivers/pilots.

show abstract

Section: B Stimulus-generated Aoismentioning

confidence: 99%

A Survey of Eye Tracking in Automobile and Aviation Studies: Implications for Eye-Tracking Studies in Marine Operations

Mao

Hildre

et al. 2021

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

show abstract

“…Continuing with the example of the autonomous car domain, several systems under research have been described in the literature that identify methods that warn drivers in order to improve situational awareness. These methods include vibration on the steering wheel [34] or the driver's seat [35], audio alerts, and visuals on the head-up display [36,37]. From these studies, Table 1 defines the possible interaction mechanisms of the car device that can be used for each obtrusiveness level.…”

Section: Managing User Attentionmentioning

confidence: 99%

Modeling and “smart” prototyping human-in-the-loop interactions for AmI environments

Gil

Albert

Fons

et al. 2021

Pers Ubiquit Comput

View full text Add to dashboard Cite

Autonomous capabilities are required in AmI environments in order to adapt systems to new environmental conditions and situations. However, keeping the human in the loop and in control of such systems is still necessary because of the diversity of systems, domains, environments, context situations, and social and legal constraints, which makes full autonomy a utopia within the short or medium term. Human-system integration introduces an important number of challenges and problems that have to be solved. On the one hand, humans should interact with systems even in those situations where their attentional, cognitive, and physical resources are limited in order to perform the interaction. On the other hand, systems must avoid overwhelming the user with unnecessary actions. Therefore, appropriate user-centered methods for AmI development should be used to help designers analyze and design human-in-the-loop interactions in AmI environments. This paper presents a user-centered design method that defines a process with a set of tools and techniques that supports the process steps in order to systematically design, prototype, and validate human-in-the-loop (HiL) solutions. The process starts with the definition of the HiL design, which defines how the system cooperates with the human. This HiL design is built using a conceptual framework that focuses on achieving human-system interactions that get human attention and avoid obtrusiveness. Then, we provide a software infrastructure to generate a prototype based on the HiL design and validate it by having end-users use a web simulator. The feedback data generated during the prototype user validation is gathered and used by a machine learning tool that infers the user’s needs and preferences. Finally, these inferences are used to automatically enhance the human-in-the-loop designs and prototypes. We have validated the proposed method through a twofold perspective: an experiment to analyze the perception of interaction designers regarding their acceptance of the design method and another experiment to evaluate the usefulness of the “smart” prototyping technique. The results obtained point out the acceptability of the proposed method by designers and the useful adaptations provided by the “smart” prototyping technique to achieve a HiL design that adapts well to users’ preferences and needs.

show abstract

“…These strategies can be described as attention-oriented and reaction-oriented alerting approaches. This literature review found that this specific naming convention was first used by (Winkler et al, 2015). In an attention-oriented approach, the ToR is designed to direct or guide drivers' attention toward relevant environmental elements (e.g., road hazards).…”

Section: Attention-oriented and Reaction-oriented Alertsmentioning

confidence: 99%

“…The benefits of reaction-oriented alerts compared to attention-oriented alerts are not consistently reported. Winkler et al (2015) directly compared different ToR which varied in their warning strategy (attention-oriented vs. reaction-oriented) and specificity (generic vs. specific). Unlike Eriksson et al (2019), they found that the content of the ToRs was not crucial, reflected by the finding that there were no overall significant differences in the number of collisions between warning types.…”

Section: Figure 3 Abstract Vs Skeuomorphic Tor Design (Frommentioning

confidence: 99%

“…Warnings need to be easily and quickly understood by drivers without capturing their visual attention for too long. When drivers do not immediately interpret the meaning of alerts the alert can become distracting and negatively impact takeover performance, as seen by Winkler et al (2015). When designing takeover requests, it is recommended that alerts adopt familiar icons and remove irrelevant features from displays (Campbell et al, 2018;Liu et al, 2022).…”

Section: Takeover Alertsmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Effects of Attention-Oriented vs. Reaction-Oriented Visual Alerts on Driver Performance during Takeover Events in Highly Automated Driving

Brightman

View full text Add to dashboard Cite

Highly automated driving requires drivers to regain vehicle control if a takeover request (ToR) is issued. This transition from passive occupant to active driver is affected by human cognitive abilities and research in this area aims to improve driver performance and safety in these situations. This research investigated the impact of ToR icons on driver takeover performance and cognitive processes (situation awareness, mental workload). Three alert designs were tested: one attention-oriented, one reaction-oriented, and one non-informative alert. In Experiment 1 (N=30), the methodology and alert designs were validated and refined. In Experiment 2, (N=45) the impact of alerting strategy on takeover performance was tested. Results indicated that reaction-oriented alerts produced the best performance and were most preferred among participants. These findings contribute to the understanding of takeover performance in highly autonomous vehicles and can be used for developing alerts that best support drivers' cognitive processing and decision-making during takeovers. I am immensely grateful to my family and friends for cheering me on and keeping me sane these past two years. Grace, Hunter and Talston, thank you for keeping me laughing throughout every peak and pit. Lastly, Harry, your relentless positivity, endless patience and unwavering belief in me made all the difference. Thank you.

show abstract

Distractive or Supportive -- How Warnings in the Head-up Display Affect Drivers' Gaze and Driving Behavior

Cited by 19 publications

References 10 publications

A Survey of Eye Tracking in Automobile and Aviation Studies: Implications for Eye-Tracking Studies in Marine Operations

A Survey of Eye Tracking in Automobile and Aviation Studies: Implications for Eye-Tracking Studies in Marine Operations

Modeling and “smart” prototyping human-in-the-loop interactions for AmI environments

Investigating the Effects of Attention-Oriented vs. Reaction-Oriented Visual Alerts on Driver Performance during Takeover Events in Highly Automated Driving

Contact Info

Product

Resources

About