Designing Human-Machine Interactions in the Automated City: Methodologies, Considerations, Principles
March 07, 2024 Β· Declared Dead Β· π Automating Cities
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Martin Tomitsch, Marius Hoggenmueller
arXiv ID
2403.04928
Category
cs.HC: Human-Computer Interaction
Citations
7
Venue
Automating Cities
Last Checked
4 months ago
Abstract
Technological progress paves the way to ever-increasing opportunities for automating city services. This spans from already existing concepts, such as automated shuttles at airports, to more speculative applications, such as fully autonomous delivery robots. As these services are being automated, it is critical that this process is underpinned by a human-centred perspective. This chapter provides a framework for future research and practice in this emerging domain. It draws on research from the field of human-computer interaction and introduces a number of methodologies that can be used to structure the process of designing interactions between people and automated urban applications. Based on research case studies, the chapter discusses specific elements that need to be considered when designing human-machine interactions in an urban environment. The chapter further proposes a model for designing automated urban applications and a set of principles to guide their prototyping and deployment.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted