车载导航大模型语音提示生成方法OA
Voice prompt generation method based on large language model for in-car navigation
车载导航系统需要语音提示既精确又具有上下文感知能力,但传统的基于规则的方法在动态交通场景中往往难以应对.本文面向车载导航大模型应用,提出了一种语音提示生成(VPG)方法,该方法在生成结构化语音指令的同时解决了若干关键技术挑战.VPG利用模板选择器来检索高质量模板,从而确保输出的可控性并符合所需结构;利用关键词短语预测器从结构化导航数据中提取关键信息,在准确性与完整性之间取得平衡;通过序列生成器生成流畅且与上下文相关的提示语.该方法可融合静态导航信息与实时交通多源数据,满足车载场景实时处理需求.实验表明,VPG在准确性、流畅性和简洁性上均优于传统方法,可为车载导航大模型语音提示提供有效技术支撑.
In-car navigation systems require voice prompts to be both precise and context-aware,but traditional rule-based methods often struggle in dynamic traffic scenarios.For the application of large language models in in-car navigation,this paper proposed a voice prompt generation(VPG)method,which solved several key technical challenges while generating structured voice instructions.VPG utilized a template selector to retrieve high-quality templates,thereby ensuring the con-trollability of output and compliance with the required structure;it utilized a keyphrase predictor to extract key information from structured navigation data to strike a balance between accuracy and completeness and generated fluent and context-relevant prompts through a sequence generator.This method could integrate static navigation information and real-time traf-fic multi-source data to meet the real-time processing requirements of in-car scenarios.Experiments on large-scale real datas-ets show that VPG outperforms traditional methods in accuracy,fluency,and conciseness and can provide effective technical support for the voice prompt generation of large language models in in-car navigation.
冯硕;冯泽邦;郭静桐;冯仲科
北京林业大学 理学院,北京 100083北京四维图新科技股份有限公司,北京 100094北京农学院 园林学院,北京 102206北京林业大学 林学院,北京 100083
天文与地球科学
车载导航语音提示数据到文本大语言模型模板检索关键信息预测
in-car navigationvoice promptdata-to-textlarge language modeltemplate retrievalkey information prediction
《北京测绘》 2026 (6)
787-794,8
北京市自然科学基金(8232038)北京林业大学5.5工程科研创新团队项目(BLRC2023A03)北京林业大学科技创新计划(XJJSKYQD202632).
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