发表机构
AMap, Alibaba Group（高德地图，阿里巴巴集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对传统逐向导航音频指令的不足，提出结合Transformer与混合专家（MoE）的云边协同深度学习框架，大幅降低车辆偏航率，是深度学习在驾驶音频导航的首次大规模应用，推动了智能交通技术发展。
AI 中文摘要
逐向（TBT）导航系统是现代驾驶体验的核心组成部分，它提供实时音频指令，引导驾驶员安全抵达目的地。然而，现有的音频指令策略通常依赖基于规则的方法，难以在信息内容与认知负荷之间取得平衡，可能导致驾驶员在复杂环境中产生困惑或错过转向。为克服这些难题，我们首先将导航指令的生成建模为多任务学习问题，方法是将音频内容分解为模块化元素的组合。接着，我们提出一种新型深度学习框架，该框架利用Transformer强大的时空信息处理能力与混合专家（MoE）的出色多任务学习能力，为逐向驾驶导航生成实时、感知上下文的音频指令。为满足模型的计算需求，我们采用云边协同架构，确保实际应用的可扩展性与实时性能。真实世界的实验结果表明，与传统方法相比，所提方法显著降低了偏航率（车辆偏离导航路线的比例），提供更清晰、更有效的音频指令。这是深度学习在驾驶音频导航中的首次大规模应用，标志着智能交通与驾驶辅助技术的重大进步。
英文摘要
Turn-by-turn (TBT) navigation systems are integral to modern driving experiences, providing real-time audio instructions to guide drivers safely to destinations. However, existing audio instruction policy often relies on rule-based approaches that struggle to balance informational content with cognitive load, potentially leading to driver confusion or missed turns in complex environments. To overcome these difficulties, we first model the generation of navigation instructions as a multi-task learning problem by decomposing the audio content into combinations of modular elements. Then, we propose a novel deep learning framework that leverages the powerful spatiotemporal information processing capabilities of Transformers and the strong multi-task learning abilities of Mixture of Experts (MoE) to generate real-time, context-aware audio instructions for TBT driving navigation. A cloud-edge collaborative architecture is implemented to handle the computational demands of the model, ensuring scalability and real-time performance for practical applications. Experimental results in the real world demonstrate that the proposed method significantly reduces the yaw rate (the proportion of vehicles deviating from navigation routes) compared to traditional methods, delivering clearer and more effective audio instructions. This is the first large-scale application of deep learning in driving audio navigation, marking a substantial advancement in intelligent transportation and driving assistance technologies.
CommentsThis paper has accepted by IEEE Transactions on Intelligent Transportation Systems
Journal refVolume: 27, Issue: 7, July 2026, Page(s): 7882 - 7892
