The Biasing of Action Selection Produces Emergent Human-Robot Interactions in Autonomous Driving

Lio, Mauro Da; Donà, Riccardo; Papini, Gastone Pietro Rosati; Plebe, Alice

doi:10.1109/lra.2021.3136646

Cited by 6 publications

(4 citation statements)

References 24 publications

(29 reference statements)

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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%

See 2 more Smart Citations

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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Section: Screeningmentioning

confidence: 99%

Section: Categorization Of Related Literaturementioning

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

View full text Add to dashboard Cite

show abstract

“…The agent ignores the human's request and remains on the right lane following . Further simulations are available in Da Lio et al ( 2022 ).…”

Section: Demonstrationsmentioning

confidence: 99%

Distributed cognition for collaboration between human drivers and self-driving cars

Plebe

Papini

Cherubini

et al. 2022

Front. Artif. Intell.

Self Cite

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This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities—the human and the autonomous agent—that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the rider-horse metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.

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Complex self-driving behaviors emerging from affordance competition in layered control architectures

Lio

Cherubini

Papini

et al. 2023

Cognitive Systems Research

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The Biasing of Action Selection Produces Emergent Human-Robot Interactions in Autonomous Driving

Cited by 6 publications

References 24 publications

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

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

Distributed cognition for collaboration between human drivers and self-driving cars

Complex self-driving behaviors emerging from affordance competition in layered control architectures

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