AI-Based Mobile Coaching Architectures for Driver Behavior and Road Safety: A Systematic Mapping and Design Framework.

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Bibliographic Details
Title:	AI-Based Mobile Coaching Architectures for Driver Behavior and Road Safety: A Systematic Mapping and Design Framework.
Authors:	Hilario, Manuel¹ fhilario@unfv.edu.pe, Paredes, Pervis¹, Maquera, Henry², Martínez, Shirley¹, Velasque, Jhosep¹
Source:	International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 8, p149-171. 23p.
Subjects:	Behavior modification, Road safety measures, Data protection, Motor vehicle driving, Software architecture, Mobile operating systems
Abstract:	A systematic mapping of artificial intelligence (AI)-based mobile driver coaching for behavior change and road safety in land transportation has been conducted following PRISMA, resulting in nine empirical studies (n = 9). The evidence is organized through a taxonomy covering intervention goals, AI techniques, data sources, deployment patterns, outcome and engagement metrics, and privacy/dependability safeguards. Across the mapped interventions, a substantial share targets specific risky behaviors, particularly phone-related distraction, reporting relative reductions of up to 21% versus controls in controlled evaluations. Deployment designs are predominantly hybrid, combining on-device inference for timely detection with server-side components for aggregation and program features. Based on the extracted evidence, a system-oriented design framework and practical guidelines are proposed to support researchers and mobile system designers in selecting architectures and evaluation metrics for interactive driver coaching. Key limitations include heterogeneous outcome definitions and predominantly short- to medium-term follow-up, which constrain comparability and generalization across settings. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	A systematic mapping of artificial intelligence (AI)-based mobile driver coaching for behavior change and road safety in land transportation has been conducted following PRISMA, resulting in nine empirical studies (n = 9). The evidence is organized through a taxonomy covering intervention goals, AI techniques, data sources, deployment patterns, outcome and engagement metrics, and privacy/dependability safeguards. Across the mapped interventions, a substantial share targets specific risky behaviors, particularly phone-related distraction, reporting relative reductions of up to 21% versus controls in controlled evaluations. Deployment designs are predominantly hybrid, combining on-device inference for timely detection with server-side components for aggregation and program features. Based on the extracted evidence, a system-oriented design framework and practical guidelines are proposed to support researchers and mobile system designers in selecting architectures and evaluation metrics for interactive driver coaching. Key limitations include heterogeneous outcome definitions and predominantly short- to medium-term follow-up, which constrain comparability and generalization across settings. [ABSTRACT FROM AUTHOR]
ISSN:	18657923
DOI:	10.3991/ijim.v20i08.59843