Human-Computer Collaborative Interaction Design of Intelligent Vehicle—A Case Study of HMI of Adaptive Cruise Control

Liu, Yujia; Zhang, Jun; Li, Yang; Hansen, Preben; Wang, Jianmin

doi:10.1007/978-3-030-78358-7_21

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…ACM Digital Library: [10,20,21,26,[34][35][36][37][38][39][40][41][42] Scopus: [22,[43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] IEEE Xplore: [61][62][63][64][65][66][67][68][69] Google Scholar: [70][71][72][73][74][75][76][77][78]…”

Section: Screeningmentioning

confidence: 99%

“…In our literature survey, we did not come across research work on this topic, despite teleoperation in automated vehicles becoming a more and more relevant research area. Discrete and Deterministic [26,[34][35][36][47][48][49][50]54,59,63,65,71,75,76] Continuous and Negotiated [10,[20][21][22]37,[39][40][41]43,45,[51][52][53]55,56,58,[60][61][62]64,[66][67][68]70,77,78] Discrete and Negotiated [38,44,46,69] Agents Task Residuals Task Level Strategical -Tactical [20][21][22]26,…”

Section: Categorization Of Related Literaturementioning

confidence: 99%

“…Input Modality Haptic [10,26,[34][35][36][37][38][39]41,[44][45][46][47][49][50][51][52][53][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]71,72,[75][76][77][78] Auditory [40] Gaze [21] Multiple Modalities [20,22,43,48,54,70] Output Modality Visual [21,22,26,34,[40]…”

Section: Human-machine Interfacementioning

confidence: 99%

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How to Design Human-Vehicle Cooperation for Automated Driving: A Review of Use Cases, Concepts, and Interfaces

Peintner,

Escher,

Detjen

et al. 2024

MTI

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Currently, a significant gap exists between academic and industrial research in automated driving development. Despite this, there is common sense that cooperative control approaches in automated vehicles will surpass the previously favored takeover paradigm in most driving situations due to enhanced driving performance and user experience. Yet, the application of these concepts in real driving situations remains unclear, and a holistic approach to driving cooperation is missing. Existing research has primarily focused on testing specific interaction scenarios and implementations. To address this gap and offer a contemporary perspective on designing human–vehicle cooperation in automated driving, we have developed a three-part taxonomy with the help of an extensive literature review. The taxonomy broadens the notion of driving cooperation towards a holistic and application-oriented view by encompassing (1) the “Cooperation Use Case”, (2) the “Cooperation Frame”, and (3) the “Human–Machine Interface”. We validate the taxonomy by categorizing related literature and providing a detailed analysis of an exemplar paper. The proposed taxonomy offers designers and researchers a concise overview of the current state of driver cooperation and insights for future work. Further, the taxonomy can guide automotive HMI designers in ideation, communication, comparison, and reflection of cooperative driving interfaces.

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