Visual Distraction Effects of In-Car Text Entry Methods

Kujala, Tuomo; Grahn, Hilkka

doi:10.1145/3122986.3122987

Cited by 15 publications

(13 citation statements)

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“…The results of this research indicate that some tasks can be extremely difficult to perform within the driving environment by simply using visual interaction, depending on driver's ability to multitask [32]. These tasks include 'Text Entry' as well as 'Multi-Layered Menu Selection' on a touchscreen-based device as suggested by Kujala and Grahn [33]. As these tasks cannot be completely removed from the systems, the interaction methods need to be carefully developed to reduce driver's visual and cognitive distraction.…”

Section: Resultsmentioning

confidence: 91%

Reducing driver distraction by improving secondary task performance through multimodal touchscreen interaction

2019

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Methods of information presentation in the automotive space have been evolving continuously in recent years. As technology pushes forward the boundaries of what is possible, automobile manufacturers are trying to keep up with the current trends. Traditionally, the often-long development and quality control cycles of the automotive sector ensured slow yet steady progress. However, the exponential advancement in the mobile and hand-held computing space seen in the last 10 years has put immense pressure on automobile manufacturers to try to catch up. For this reason, we now see manufacturers trying to explore new techniques for in-vehicle interaction (IVI), which were ignored in the past. However, recent attempts have either simply extended the interaction model already used in mobile or handheld computing devices or increased visual-only presentation-of-information with limited expansion to other modalities (i.e. audio or haptics). This is also true for system interaction which generally happens within complex driving environments, making the primary task of a driver (driving) even more challenging. Essentially, there is an inherent need to design and research IVI systems that complement and natively support a multimodal interaction approach, providing all the necessary information without increasing driver's cognitive load or at a bare minimum his/her visual load. In this research we focus on the key elements of IVI system: touchscreen interaction by developing prototype devices that can complement the conventional visual and auditory modalities in a simple and natural manner. Instead of adding primitive touch feedback cues to increase redundancy or complexity, we approach the issue by looking at the current requirements of interaction and complementing the existing system with natural and intuitive input and output methods, which are less affected by environmental noise than traditional multimodal systems.

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Section: Resultsmentioning

confidence: 91%

Reducing driver distraction by improving secondary task performance through multimodal touchscreen interaction

2019

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“…To achieve successful and natural interaction between users and devices, the technologies to accurately understand user intent has become all the more important. For example, recent interactive systems attempt to detect user intents expressed in the form of gestures or voice commands using signals from various sensing devices [1][2][3]. More immersive and natural ways to capture user intent include face orientation estimation [4][5][6], body tracking [7][8][9], and estimation of gaze direction [10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the images that were modified by adjusting their intensity appear to be merely hazy, rather than brighten. Conversely, the gamma correction methods successfully enhanced the images in Low i and Low g , such that the resultant images of the eye contained little noise and had well-defined appearances (see row (1) and (2) in Figure 12). However, these methods failed to enhance the images in the remaining test sets (see row (3)-(7) in Figure 12).…”

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confidence: 99%

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Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks

Kim

Jeong

2020

Sensors

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In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user’s intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered one of the most effective indicators among recently developed interaction techniques (e.g., face orientation estimation, body tracking, and gesture recognition). Previous gaze estimation techniques, however, are known to be effective only in a controlled lab environment under normal lighting conditions. In this study, we propose a novel deep learning-based approach to achieve a successful gaze estimation under various low-light conditions, which is anticipated to be more practical for smart interaction scenarios. The proposed approach utilizes a generative adversarial network (GAN) to enhance users’ eye images captured under low-light conditions, thereby restoring missing information for gaze estimation. Afterward, the GAN-recovered images are fed into the convolutional neural network architecture as input data to estimate the direction of the user gaze. Our experimental results on the modified MPIIGaze dataset demonstrate that the proposed approach achieves an average performance improvement of 4.53%–8.9% under low and dark light conditions, which is a promising step toward further research.

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“…According to several studies, text entry with a touch screen keyboard is among the most visually distracting secondary tasks for the driver (Kujala, 2017;Tsimhoni et al, 2004). In comparison to speech input, touch leads to longer eyes-off the road time and a greater frequency of long glances off the road (Garay-Vega et al, 2010).…”

Section: Direct-touch Input In the Automotive Domainmentioning

confidence: 99%

Natural Multimodal Interaction in the Car - Generating Design Support for Speech, Gesture, and Gaze Interaction while Driving

Roider¹

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Driving a modern car is more than just maneuvering the vehicle on the road. At the same time, drivers want to listen to music, operate the navigation system, compose, and read messages and more. Future cars are turning from simple means for transportation into smart devices on wheels. This trend will continue in the next years together with the advent of automated vehicles. However, technical challenges, legal regulations, and high costs slow down the penetration of automated vehicles. For this reason, a great majority of people will still be driving manually at least for the next decade. Consequently, it must be ensured that all the features of novel infotainment systems can be used easily, efficiently without distracting the driver from the task of driving and still provide a high user experience. A promising approach to cope with this challenge is multimodal in-car interaction. Multimodal interaction basically describes the combination of different input and output modalities for driver-vehicle interaction. Research has pointed out the potential to create a more flexible, efficient, and robust interaction. In addition to that, the integration of natural interaction modalities such as speech, gestures and gaze, the communication with the car could increase the naturalness of the interaction. Based on these advantages, the researcher community in the field of automotive user interfaces has produced several interesting concepts for multimodal interaction in vehicles. The problem is that the resulting insights and recommendations are often easily applicable in the design process of other concepts because they too concrete or very abstract. At the same time, concepts focus on different aspects. Some aim to reduce distraction while others want to increase efficiency or provide a better user experience. This makes it difficult to give overarching recommendations on how to combine natural input modalities while driving. As a consequence, interaction designers of in-vehicle systems are lacking adequate design support that enables them to transfer existing knowledge about the design of multimodal in-vehicle applications to their own concepts. This thesis addresses this gap by providing empirically validated design support for multimodal in-vehicle applications. It starts with a review of existing design support for automotive and multimodal applications. Based on that we report a series of user experiments that investigate various aspects of multimodal in-vehicle interaction with more than 200 participants in lab setups and driving simulators. During these experiments, we assessed the potentials of multimodality while driving, explored how user interfaces can support speech and gestures, and evaluated novel interaction techniques. The insights from these experiments extend existing knowledge from literature in order to create the first pattern collection for multimodal natural in-vehicle interaction. The collection contains 15 patterns that describe solutions for reoccurring problems when combining natural input with speech, gestures, or gaze in the car in a structured way. Finally, we present a prototype of an in-vehicle information system, which demonstrates the application of the proposed patterns and evaluate it in a driving-simulator experiment. This work contributes to field of automotive user interfaces in three ways. First, it presents the first pattern collection for multimodal natural in-vehicle interaction. Second, it illustrates and evaluates interaction techniques that combine speech and gestures with gaze input. Third, it provides empirical results of a series of user experiments that show the effects of multimodal natural interaction on different factors such as driving performance, glance behavior, interaction efficiency, and user experience.

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Visual Distraction Effects of In-Car Text Entry Methods

Cited by 15 publications

References 20 publications

Reducing driver distraction by improving secondary task performance through multimodal touchscreen interaction

Reducing driver distraction by improving secondary task performance through multimodal touchscreen interaction

Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks

Natural Multimodal Interaction in the Car - Generating Design Support for Speech, Gesture, and Gaze Interaction while Driving

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