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This research applies Transformer-based deep learning and Mixture of Experts architectures to optimize turn-by-turn navigation audio, addressing the limitations of traditional rule-based systems in complex driving environments. By utilizing a cloud-edge collaborative architecture, the study achieves real-time performance that significantly reduces vehicle deviation rates compared to legacy methods. This marks a pivotal shift from static scripting to dynamic, context-aware instruction generation for intelligent transportation systems.
Revolutionizing Turn-by-Turn Navigation with Cloud-Edge Deep Learning
arXiv:2608.29073v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2608.29073 [cs.AI]
(or arXiv:2608.29073v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.29073 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1109/TITS.2026.3662685 Focus to learn more DOI(s) linking to related resources
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