ChaseLight

Meschtscherjakov, Alexander; Döttlinger, Christine; Rödel, Christina; Tscheligi, Manfred

doi:10.1145/2799250.2799279

Cited by 33 publications

(7 citation statements)

References 11 publications

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“…While visual information presented on the dashboard during manual driving may be detectable using peripheral vision (Lamble, Laakso, & Summala, 1999), such information might be missed if the driver is engaged in a non-driving task. A promising approach for conveying visual messages is to use ambient TORs, for example by lighting up a LED strip or bar under the windshield, along the A-pillar, or around the cabin (e.g., Kelsch & Dziennus, 2015;Löcken, Heuten, & Boll, 2015;Meschtscherjakov, Döttlinger, Rödel, & Tscheligi, 2015;Pfromm, Cieler, & Bruder, 2013;Winkler et al, 2018), or by using spatially directed LED strips (Dettman & Bullinger, 2017). Such ambient TORs might be more easily detected by the distracted driver than localized visual cues.…”

Section: Visual Displaysmentioning

confidence: 99%

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Bazilinskyy

Petermeijer

Petrovych

et al. 2018

Transportation Research Part F: Traffic Psychology and Behaviou

128

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An important research question in the domain of highly automated driving is how to aid drivers in transitions between manual and automated control. Until highly automated cars are available, knowledge on this topic has to be obtained via simulators and self-report questionnaires. Using crowdsourcing, we surveyed 1692 people on auditory, visual, and vibrotactile take-over requests (TORs) in highly automated driving. The survey presented recordings of auditory messages and illustrations of visual and vibrational messages in traffic scenarios of various urgency levels. Multimodal TORs were the most preferred option in high-urgency scenarios. Auditory TORs were the most preferred option in lowurgency scenarios and as a confirmation message that the system is ready to switch from manual to automated mode. For low-urgency scenarios, visual-only TORs were more preferred than vibration-only TORs. Beeps with shorter interpulse intervals were perceived as more urgent, with Stevens' power law yielding an accurate fit to the data. Spoken messages were more accepted than abstract sounds, and the female voice was more preferred than the male voice. Preferences and perceived urgency ratings were similar in middle-and high-income countries. In summary, this international survey showed that people's preferences for TOR types in highly automated driving depend on the urgency of the situation.

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Section: Visual Displaysmentioning

confidence: 99%

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Bazilinskyy

Petermeijer

Petrovych

et al. 2018

Transportation Research Part F: Traffic Psychology and Behaviou

128

View full text Add to dashboard Cite

show abstract

“…Autonomous Vehicles (AUV) [74][75][76] is considered as potential solutions to various unprocessed Big-Data [77], network congestion and pollution issues [78]. Academicians [79][80][81][82] had made efforts to develop new interfaces that do not rely on the driver's vision as in the case with conventional screen-based interfaces to reduce the amount of data generated every second in respect to this setup. A detailed survey over AUV in expediting the application over the smart highway regarding data is presented by Their proposed model claims to overcome the issues of VANET.…”

Section: N O D E S Heterogeneitymentioning

confidence: 99%

Accessible review of internet of vehicle models for intelligent transportation and research gaps for potential future directions

Garg

Mehrotra

Pandey

et al. 2021

Peer-to-Peer Netw. Appl.

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This paper presents a state-of-the-art review of various models of Internet of Vehicles (IoV) that were proposed to revolutionize the transportation and automation industry. In the last few years, IoV technology has emerged as a global revolution, demonstrating considerable impact upon the transportation and automation industry, hence, the interest of the researchers bends towards the utility of the IoV technology which motivated us to present a comprehensive review. This paper, thus, collates recent findings along with some anticipated applications of IoV technologies for transportation. It brings to light the evolution of the IoV studies referred as models for transportation since its birth and aims to present a deep insightful review of various IoV technologies with their strengths and weaknesses. The reviewed literature is organized systematically to give a categorical overview of recent developments. This article also discusses the outstanding potentialities and challenges that exist in transportation from IoV perspective with the belief that these will have the capacity to be addressed in the near future. As there is no review literature on IoV technology available for transportation. Hence, we strongly believe that this study will be useful and enable the researchers to utilize IoV technologies to solve complex outstanding problems in transportation and automation. Keywords Internet of vehicles . V2X . network . Energy efficiency . Big data . QoS 1 IoV is abbreviated as Internet of Vehicles which has also been referred to by various names like ICV (Internet Connected Vehicles) or CV (Connected Vehicles) or AUV (Autonomous Vehicles) by other academicians. 2 V2X represents V2V, V2I, V2H, V2R and V2C which is also referred to as V2α by other academicians.

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“…A screen-based display might be better suited to retrieve information about the car on demand. Nevertheless, at least for speed, a concept using lights in the A-pillars, called "ChaseLights" [33], could be a possible solution for continuous feedback that does not require a screen.…”

Section: Implications For Further Iterationsmentioning

confidence: 99%

Increasing User Experience and Trust in Automated Vehicles via an Ambient Light Display

Löcken

Frison

Fahn

et al. 2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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ChaseLight

Cited by 33 publications

References 11 publications

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Accessible review of internet of vehicle models for intelligent transportation and research gaps for potential future directions

Increasing User Experience and Trust in Automated Vehicles via an Ambient Light Display

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