Research on the Application of Artificial Intelligence in Scenic Mobility Scooter Context-Aware Design Methodology

In order to explore the design application of context-awareness in the scenic mobility scooter under the background of artificial intelligence. Firstly, the concepts of artificial intelligence, context-awareness and scenic mobility scooter are introduced, and the current status of theoretical research and existing problems of scenic mobility scooter are sorted out. Secondly, combining the advantages and characteristics of context-aware intervention in the scenic scooter, the context information related to the scenic scooter under the view of artificial intelligence is divided into four categories: user, environment, task, and equipment, and at the same time, different stages of the use of the context information hierarchy is categorized and corresponds to the pain point needs, and the corresponding artificial intelligence access points are analyzed. Finally, four design strategies are proposed based on the above four contextual factors. It is concluded that the application of context-awareness theory to the design of scenic mobility scooters under the perspective of artificial intelligence strengthens the ability to perceive multidimensional contextual information, explores the user's needs in dynamic situations from a multidimensional perspective, simplifies the user's operation path, and enhances the user's touring experience.

Authors

Institutions

Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1145/3712623.3712664
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on the Application of Artificial Intelligence in Scenic Mobility Scooter Context-Aware Design Methodology

Jiayi Yang, Liping Liu, Jie Zhang, Yang Hui Cai
Traffic Prediction and Management Techniques
article

Research on the Application of Artificial Intelligence in Scenic Mobility Scooter Context-Aware Design Methodology

Jiayi Yang, Liping Liu, Jie Zhang, Yang Hui Cai
article en

Abstract

In order to explore the design application of context-awareness in the scenic mobility scooter under the background of artificial intelligence. Firstly, the concepts of artificial intelligence, context-awareness and scenic mobility scooter are introduced, and the current status of theoretical research and existing problems of scenic mobility scooter are sorted out. Secondly, combining the advantages and characteristics of context-aware intervention in the scenic scooter, the context information related to the scenic scooter under the view of artificial intelligence is divided into four categories: user, environment, task, and equipment, and at the same time, different stages of the use of the context information hierarchy is categorized and corresponds to the pain point needs, and the corresponding artificial intelligence access points are analyzed. Finally, four design strategies are proposed based on the above four contextual factors. It is concluded that the application of context-awareness theory to the design of scenic mobility scooters under the perspective of artificial intelligence strengthens the ability to perceive multidimensional contextual information, explores the user's needs in dynamic situations from a multidimensional perspective, simplifies the user's operation path, and enhances the user's touring experience.

East China University of Science and Technology (CN)
Openalex Percentile: Top 71%
Traffic Prediction and Management Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.