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

Hilario M.; Paredes P.; Maquera H.; Martínez S.; Velasque J. · International Journal of Interactive Mobile Technologies (2026) · DOI: 10.3991/ijim.v20i08.59843

Resumen

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.

Instituciones: Universidad Nacional Federico Villarreal; Universidad Nacional del Centro del Perú