Problem
Research questions and friction points this paper is trying to address.
localisation
driver coaching
telematics
regulation
infrastructure
One Feedback System Does Not Fit All: Localising Data-to-Text Driver Coaching for the United Kingdom and Nigeria
🤖 AI Summary
本文针对不同地区驾驶辅导内容本地化问题，通过比较英国和尼日利亚的数据到文本驾驶辅导系统，提出基于当地知识、风险、法规等定制化建议的方法。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
localisation
driver coaching
contextual feedback
safety education
data-to-text
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