为尼日利亚设计可本地化驾驶反馈的端到端AI系统
An End-to-End System for Culturally-Attuned Driving Feedback using a Dual-Component NLG Engine
- 双组件自然语言生成引擎，结合法律规范与行为心理学
- 90名司机试点中有效识别不安全驾驶行为，支持离线运行
- 专用于检测酒驾，适合低资源、弱网络环境应用
本文提出一个端到端移动系统，为尼日利亚驾驶员提供符合当地文化的行车安全反馈。系统核心是创新的双组件自然语言生成（NLG）引擎，可输出基于法律的安全建议和基于行为理论的说服性报告。完整架构包括自动行程检测服务、本地化行为分析及两步反思式NLG流程，确保反馈质量。系统还集成专用机器学习模型，用于检测酒驾这一关键本地安全问题。整体设计具备抗断网和噪声传感器数据的能力。对90名司机的试点部署验证了其可行性，初步结果显示能有效识别不安全行为。该研究为数据到文本与AI系统实现社会价值提供了可复用框架。
原文摘要 · Abstract (English)
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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