Unimodal and Multimodal Signals to Support Control Transitions in Semiautonomous Vehicles

Salminen, Katri; Farooq, Ahmed; Rantala, Jussi; Surakka, Veikko; Raisamo, Roope

doi:10.1145/3342197.3344522

Cited by 16 publications

(11 citation statements)

References 44 publications

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“…The third area of study focuses on cross-modality comparisons. Similar to the cross-modality comparisons conducted in manual driving [7,30], a number of studies [5,14,31,32] have investigated which modality is more effective at triggering driver responses during control transitions in partial autonomy. However, these studies occasionally produce contradictory results.…”

Section: Takeover Request and Modalitymentioning

confidence: 99%

Individual Differences in Signal Perception for Takeover Request in Autonomous Driving

Lee

Kang

2023

Applied Sciences

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In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. For this objective, the research examined the correlations between age, gender, and individual backgrounds with reaction times to TOR signals, investigating the effects of sub-attribute variations on participants’ responses and exploring the subjective evaluations of the signals. A driving simulator was utilized to create a realistic driving environment and measure participants’ reaction times in takeover request situations. Analysis of the data revealed correlations between age and reaction times for auditory and tactile signals, with interaction effects observed between age and sub-attribute intensity. Additionally, participants exhibited varying reaction time patterns in response to different sub-attribute intensities. By evaluating individual differences in perception based on modality characteristics, often overlooked in prior research, this study serves as a foundational contribution to future research in the field.

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Section: Takeover Request and Modalitymentioning

confidence: 99%

Individual Differences in Signal Perception for Takeover Request in Autonomous Driving

Lee

Kang

2023

Applied Sciences

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“…Moreover, interactions with visual or audio-visual displays are more efficient than those with auditory displays only [36]. In this sense, research on multimodal perception is particularly relevant when studying human factors of driver aid systems [37,38].…”

Section: Human Factors and Their Limitsmentioning

confidence: 99%

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

Enjalbert

Gandini

Baños³

et al. 2021

Machines

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This paper provides an overview of Human Machine Interface (HMI) design and command systems in commercial or experimental operation across transport modes. It presents and comments on different HMIs from the perspective of vehicle automation equipment and simulators of different application domains. Considering the fields of cognition and automation, this investigation highlights human factors and the experiences of different industries according to industrial and literature reviews. Moreover, to better focus the objectives and extend the investigated industrial panorama, the analysis covers the most effective simulators in operation across various transport modes for the training of operators as well as research in the fields of safety and ergonomics. Special focus is given to new technologies that are potentially applicable in future train cabins, e.g., visual displays and haptic-shared controls. Finally, a synthesis of human factors and their limits regarding support for monitoring or driving assistance is proposed.

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“…User shows how people might trust the system and how the auto-mobile office might affect their wellbeing and privacy. (Salminen, Farooq, Rantala, Surakka, & Raisamo, 2019).…”

Section: Affinity Diagrammentioning

confidence: 99%

“…Affinity diagram for auto-mobile office Modality AuditoryVoice dictation errors are hard to correct.The responses are slower compared to other modalities(Salminen, Farooq, Rantala, Surakka, & Raisamo, 2019). Visual…”

mentioning

confidence: 90%

Defining A Design Space of The Auto-Mobile Office: A Computational Abstraction Hierarchy Analysis

Katrahmani

Kamaraj

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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One advantage of highly automated vehicles is drivers can use commute time for non-driving tasks, such as work-related tasks. The potential for an auto-mobile office—a space where drivers work in automated vehicles—is a complex yet underexplored idea. This paper begins to define a design space of the auto- mobile office in SAE Level 3 automated vehicles by integrating the affinity diagram (AD) with a computational representation of the abstraction hierarchy (AH). The AD uses a bottom-up approach where researchers starting with individual findings aggregate and abstract those into higher-level concepts. The AH uses a top-down approach where researchers start with first principles to identify means-ends links between system goals and concrete forms of the system. Using the programming language R, the means-ends links of AH can be explored statistically. This computational approach to the AH provides a systematic means to define the design space of the auto-mobile office.

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Unimodal and Multimodal Signals to Support Control Transitions in Semiautonomous Vehicles

Cited by 16 publications

References 44 publications

Individual Differences in Signal Perception for Takeover Request in Autonomous Driving

Individual Differences in Signal Perception for Takeover Request in Autonomous Driving

Human–Machine Interface in Transport Systems: An Industrial Overview for More Extended Rail Applications

Defining A Design Space of The Auto-Mobile Office: A Computational Abstraction Hierarchy Analysis

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