A multistudy investigation of drivers and passengers’ gesture and voice input preferences for in-vehicle interactions

Bilius, Laura-Bianca; Vatavu, Radu-Daniel

doi:10.1080/15472450.2020.1846127

Cited by 24 publications

(5 citation statements)

References 80 publications

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“…Females are more inclined than males to accept and use touch buttons, touchscreen, and voice control for music playback. This finding complements previous research that explored age differences in preferences for voice and gesture interactions [ 5 ]. Furthermore, the acceptance, perceived ease of use, and self-efficacy for using voice control to play music were significantly higher than other control methods.…”

Section: Discussionsupporting

confidence: 86%

“…This has sparked ongoing innovation in systems, control devices, and features. The goal is to make travel more comfortable and enjoyable for drivers and passengers [ 5 ]. Additionally, Detjen and his colleagues highlight that higher-level autonomous vehicles will become a reality within decades, and vehicle automation will have a significant impact on in-vehicle interaction, expanding the range of human-vehicle interactions [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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An empirical study on new-energy vehicle users’ preferences of in-vehicle interaction input methods

Sun,

Wang,

Miao

et al. 2024

Heliyon

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

An empirical study on new-energy vehicle users’ preferences of in-vehicle interaction input methods

Sun,

Wang,

Miao

et al. 2024

Heliyon

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“…VCSs provide an accurate set of voice notes, ensuring a high accuracy rate in the data processing system. VCSs use intelligent techniques to learn how to interact with people and recognize users' voice commands while travelling in AVs, providing uninterrupted and accurate user services [14]. A VCS is installed in AVs to capture users' voice commands and securely store data for later use.…”

Section: Introductionmentioning

confidence: 99%

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

Anjum

Shahab

2023

Sustainability

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In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform driving tasks. Therefore, their quality and safety are critical concerns for driving users. AVs use advanced driver assistance systems (ADASs) that heavily rely on sensors and camera data. These data are processed to execute vehicle control functions for autonomous driving. Furthermore, AVs have a voice communication system (VCS) to interact with driving users to accomplish different hand-free functions. Some functions such as navigation, climate control, media and entertainment, communication, vehicle settings, vehicle status, and emergency assistance have been successfully incorporated into AVs using VCSs. Several researchers have also implemented vehicle control functions using voice commands through VCSs. If a situation occurs when AV has lost control due to malfunctioning or fault in the installed computer, sensors and other associated modules, driving users can control the AV using voice notes to perform some driving tasks such as changing speeds, lanes, breaking, and directing the car to reach a safe condition. Furthermore, driving users need manual control over AV to perform these tasks in some situations, like lane changing or taking an exit due to divergence. These tasks can also be performed with the help of voice commands using VCSs. Therefore, finding the exact voice note used to instruct different actuators in risk situations is crucial. As a result, VCSs can greatly improve safety in critical situations where manual intervention is necessary. AVs’ functions and quality can be significantly increased by integrating a VCS with an ADAS and developing an interactive ADAS. Now, the driver functions are controlled by voice features. Therefore, natural language processing is utilized to extract the features to determine the user’s requirements. The extracted features control the vehicle functions and support driving activities. The existing techniques consume high computation while predicting the user command and causing a reduction in the AVs’ functions. This research issue is overcome by applying the variation continuous input recognition model. The proposed approach utilizes the linear training process that resolves the listening and time-constrained problems and uncertain response issues. The proposed model categorizes the inputs into non-trainable and trainable data, according to the data readiness and listening span. Then, the non-distinguishable data were validated by dividing it into the linear inputs used to improve the response in the AVs. Thus, effectively utilizing training parameters and the data decomposition process minimizes the uncertainty and increases the response rate. The proposed model has significantly improved the exact prediction of users’ voice notes and computation efficiency. This improvement enhances the VCS quality and reliability used to perform hand-free and vehicle control functions. The reliability of these functions ultimately improves the safety of AVs’ driving users and other road users.

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“…Finger augmentation devices (FADs) are finger-worn electronic tools conceived to provide supplemental capabilities through an additional augmentation other than their form [1]. FADs can have different form factors, spanning from proximalphalanx rings [2] to nail-pads [3], [4], and several possible applications, like driving vehicles [5], [6] or controlling aerial drones [7]. The most ambitious goal of FADs is the achievement of a sensorial ultrability, namely providing the user with an artificial sensation to either restore a lost or degraded natural ability or even to add a new sense, going beyond the natural physical feelings of humans [8].…”

Section: Introductionmentioning

confidence: 99%

Sensing Performance of Multi-Channel RFID-Based Finger Augmentation Devices for Tactile Internet

Naccarata

Bianco

Marrocco

2022

IEEE J. Radio Freq. Identif.

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Radiofrequency finger augmentation devices (R-FADs) are a recently introduced class of epidermal radiofrequency identification (RFID) sensor-tags attached to the fingers, communicating with a body-worn reader. These devices are promising candidates to enable Tactile Internet (TI) applications in the short term. R-FAD based on auto-tuning RFID microchips can be used as dielectric probes for the material of touched objects. However, due to the nearly unpredictable intrinsic variability of finger-object interaction, a single sensorized finger (single-channel device) is not enough to guarantee reliable data sampling. These limitations can be overcome by exploiting a multi-channel R-FAD sensorizing multiple fingers of the hand. In this paper, the dielectric-sensing performance of a multi-channel R-FAD, composed of sensors encapsulated into soft elastomers, is numerically and experimentally characterized, involving a set of volunteers. The inter-sensor coupling is negligible, thus enabling simultaneous independent dielectric measurements. The multi-sensor configuration allows for 100% reliability of the onhand communication link for touched objects in a wide range of permittivity. Experiments moreover demonstrate that multichannel measurements can halve the measurement uncertainty of the single-channel case. The achievable precision is suitable to discriminate among low-, medium-, and high-permittivity materials.

show abstract

A multistudy investigation of drivers and passengers’ gesture and voice input preferences for in-vehicle interactions

Cited by 24 publications

References 80 publications

An empirical study on new-energy vehicle users’ preferences of in-vehicle interaction input methods

An empirical study on new-energy vehicle users’ preferences of in-vehicle interaction input methods

Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model

Sensing Performance of Multi-Channel RFID-Based Finger Augmentation Devices for Tactile Internet

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