An End-to-End System for Culturally-Attuned Driving Feedback using a Dual-Component NLG Engine

• URL: http://arxiv.org/abs/2509.04478v1
• Date: Sat, 30 Aug 2025 16:35:53 GMT
• Title: An End-to-End System for Culturally-Attuned Driving Feedback using a Dual-Component NLG Engine
• Abstract summary: This paper presents an end-to-end mobile system that delivers culturally-attuned safe driving feedback to drivers in Nigeria.<n>The core of the system is a novel dual-component Natural Language Generation engine that provides both legally-grounded safety tips and persuasive, theory-driven behavioural reports.<n>The architecture is engineered for robustness against intermittent connectivity and noisy sensor data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents an end-to-end mobile system that delivers culturally-attuned safe driving feedback to drivers in Nigeria, a low-resource environment with significant infrastructural challenges. The core of the system is a novel dual-component Natural Language Generation (NLG) engine that provides both legally-grounded safety tips and persuasive, theory-driven behavioural reports. We describe the complete system architecture, including an automatic trip detection service, on-device behaviour analysis, and a sophisticated NLG pipeline that leverages a two-step reflection process to ensure high-quality feedback. The system also integrates a specialized machine learning model for detecting alcohol-influenced driving, a key local safety issue. The architecture is engineered for robustness against intermittent connectivity and noisy sensor data. A pilot deployment with 90 drivers demonstrates the viability of our approach, and initial results on detected unsafe behaviours are presented. This work provides a framework for applying data-to-text and AI systems to achieve social good.

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