You Do Not Have to Touch to Select

Ahmad, Bashar I.; Langdon, Patrick; Godsill, Simon; Donkor, Richard; Wilde, Rebecca; Skrypchuk, Lee

doi:10.1145/3003715.3005461

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…Therefore, one of the most explored approaches was to rely on multimodal interfaces with an haptic component in the IS screen [8]- [13]. Other approaches focused on the design of the IS interface to facilitate in-vehicle interaction (e.g [14], [15]), which could for example predict the driver's input [16], or adapt the IS interface to the driver's intent [17]. The unique vehicle setting also provided opportunities for feedback, such as feedback in the steering wheel [18]- [20] or seat-belt [20].…”

Section: Related Workmentioning

confidence: 99%

“…Other authors explored techniques which implemented indirect interaction with the IS, where the driver can point at the screen and interact with it without touching it. Ahmad et al [16] developed a predictive system, using a motion controller that detects which item the user will select early in the pointing gesture. The study determined that the time to select a target was reduced by 39% compared to traditional touchscreens.…”

Section: B Interacting With the Ismentioning

confidence: 99%

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HapWheel: In-Car Infotainment System Feedback Using Haptic and Hovering Techniques

Quintal

Lima

2022

IEEE Trans. Haptics

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In-car devices are growing both in complexity and capacity, integrating functionalities that used to be divided among other controls in the vehicles. These systems appear increasingly in the form of touchscreens as a cost-saving measure. Screens lack the physicality of traditional buttons or switches, requiring drivers to look away from the road to operate them. This paper presents the design, implementation, and two studies that evaluated HapWheel, a system that provides the driver with haptic feedback in the steering wheel while interacting with an Infotainment System. Results show that the proposed system reduced both the duration of and the number of times a driver looked away from the road. HapWheel was also successful at reducing the number of mistakes during the interaction.

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Section: Related Workmentioning

confidence: 99%

Section: B Interacting With the Ismentioning

confidence: 99%

HapWheel: In-Car Infotainment System Feedback Using Haptic and Hovering Techniques

Quintal

Lima

2022

IEEE Trans. Haptics

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“…Although studies on behavioral aspects of gestures for IVIS have been published recently, many of these studies have focused on gestures for touch-sensitive interfaces (e.g., Ahmad et al, 2016; Burnett et al, 2013; Ecker et al, 2010) or gestural interactions near or on the steering wheel (e.g., Angelini et al, 2014; Döring et al, 2011; Fang & Ainsworth, 2012; Lee et al, 2015; Mahr et al, 2011; Werner, 2014). Some works have investigated midair GBI from a user perspective, such as users’ preferences for gesture set design and feedback techniques (e.g., May et al, 2017; März et al, 2016; Riener et al, 2013; Shakeri et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Effects of Gesture-Based Interaction on Driving Behavior: A Driving Simulator Study Using the Projection-Based Vehicle-in-the-Loop

Graichen

Krems

2020

Hum Factors

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Objective We observe the driving performance effects of gesture-based interaction (GBI) versus touch-based interaction (TBI) for in-vehicle information systems (IVISs). Background As a contributing factor to a number of traffic accidents, driver distraction is a significant problem for traffic safety. More specifically, visual distraction has a strong negative impact on driving performance and risk perception. Thus, the implementation of new interaction systems that use midair gestures to encourage glance-free interactions could reduce visual distraction among drivers. Methods In this experiment, participants drove a projection-based Vehicle-in-the-Loop. The projection-based technology combines a visual simulation with kinesthetic, vestibular, and auditory feedback from a car on a test track. While driving, participants used GBI or TBI to perform IVIS tasks. To investigate driving behavior related to critical driving situations and car-following maneuvers, vehicle data based upon longitudinal and lateral driving were collected. Results Participants reacted faster to critical driving situations when using GBI compared to TBI. For drivers using TBI, steering performance decreased and time headway to a preceding vehicle was higher. Conclusion Gestures provide a safe alternative to in-vehicle interactions. Moreover, GBI has fewer effects on driver distraction than TBI. Application Potential applications of this research include all in-vehicle interaction systems used by drivers.

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“…The increased accuracy of the system might instead lead to general reduction of driver demands, since the correction of wrong selections would draw more of the user's attention. In this context, it has been shown that the enhancement of mid-air selection based on additional data results in reduced driver demands (Ahmad, Langdon, Godsill, Donkor, & Wilde, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Future work should focus on the development of a more elaborate fusion algorithm, in order to integrate gaze information based on a probabilistic model. Such approaches have been presented for the incorporation of vehicle data to optimize pointing and touch performance while driving (Ahmad et al, 2016;European Statement of Principles (ESoP), 2008;Mayer, Le, et al, 2018). The results are derived from a relatively specific setup, namely four large elements on a large screen.…”

Section: Limitationsmentioning

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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You Do Not Have to Touch to Select

Cited by 24 publications

References 21 publications

HapWheel: In-Car Infotainment System Feedback Using Haptic and Hovering Techniques

HapWheel: In-Car Infotainment System Feedback Using Haptic and Hovering Techniques

Effects of Gesture-Based Interaction on Driving Behavior: A Driving Simulator Study Using the Projection-Based Vehicle-in-the-Loop

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

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