一个反馈系统并不适用于所有场景：英国和尼日利亚数据到文本驾驶教练的本地化

中文摘要 AI 辅助

数据到文本的驾驶教练通常被呈现为从远程信息处理事件到建议的通用流程。本文认为其内容需要本地化，因为有用性和可信度取决于驾驶员的知识、常见风险、法规、基础设施和可用数据。通过追溯需求到内容选择、生成和现场评估的过程，比较了在英国和尼日利亚独立开发的两个系统。英国系统优先考虑行程后反思、与道路和地点背景相关的解释以及语气敏感的措辞。尼日利亚系统将基于检测事件的、具有法律依据的每日提示与每周说服性报告相结合；它强调安全教育和与酒精相关的风险，以应对正式培训和交通规则知识方面的报告缺口以及当地道路安全优先事项。可靠的速度限制元数据支持了英国的超速反馈，而其稀缺性导致尼日利亚评估将超速排除在结果指标之外。两项干预措施在各自的现场研究中都与降低距离归一化的不安全事件率相关，尽管其设计和指标排除了效应量比较。该分析产生了一个从需求到内容的设计过程，用于本地化安全关键的自然语言生成，而不将高收入部署视为默认。

英文摘要

Data-to-text driver coaching is often presented as a generic pipeline from telematics events to advice. This paper argues that its content requires localisation because usefulness and credibility depend on drivers' knowledge, prevalent risks, regulation, infrastructure, and available data. Two independently developed systems in the United Kingdom and Nigeria are compared by tracing requirements through content selection, generation, and field evaluation. The UK system prioritises post-trip reflection, explanations tied to road and place context, and tone-sensitive wording. The Nigerian system combines legally grounded, once-daily Tips based on detected events with weekly persuasive Reports; it foregrounds safety education and alcohol-related risk in response to reported gaps in formal training and traffic-rule knowledge, as well as local road-safety priorities. Reliable speed-limit metadata supported speeding feedback in the UK, whereas its scarcity led the Nigerian evaluation to exclude speeding from its outcome metric. Both interventions were associated with reduced distance-normalised unsafe-event rates in their own field studies, although their designs and metrics preclude an effect-size comparison. The analysis yields a requirements-to-content design process for localising safety-critical NLG without treating a high-income deployment as the default.

发表机构

• University of Aberdeen（阿伯丁大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted to INLG 2026