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.
Figures & tables
Fig. 1: TBT driving navigation. The figure displays the essential cue content and key terminology used in the TBT driving navigation, along with the paradigm of the audio instruction while the car moves through the path.
Fig. 2: Overview of TBT navigation instruction model. In the dashed box is the proposed navigation instruction model. The dashed red box represents the model component, the solid blue box represents the edge-side data embedding, and the solid green box represents the cloud-side data embedding. To the left of the dashed box is the engineering design for model real-time playing and data preparation. Abbreviations: MoE means Mixture of Experts, MLP means Multi-Layer Perceptron, MHA means Multi-Head Attention.
Fig. 3: Cloud-edge collaboration framework. The left side shows the user’s driving behavior, and the edge device in the middle determines whether it needs to generate audio instruction based on the driving progress, as well as the start of each segment requesting cloud feature embedding from the cloud server on the right side.
Fig. 4: Real-world A/B test results. Green represents the sequence model and orange represents the deployed HMM policy. (a) Yaw rate, lower yaw rates represent more effective audio instruction; (b) Words density, lower word density means more streamlined audio; (c) Main action element play rate, higher means more frequent TBT message alerts.
In-vehicle music can serve as an adaptive interface to enhance driver experience, attention, and well-being. We present Drive-to-Music, a context-aware system that generates music in real time from multimodal driving signals. Using dashcam imagery and vehicle telemetry, the system extracts scene semantics and driving context, maps them to high-level musical descriptors, and conditions generative audio models to produce contextually aligned soundtracks. The architecture combines perception and generative components to translate visual and kinematic inputs into structured musical attributes and synthesize audio with low latency. It supports smooth transitions as driving conditions evolve, and to ensure robustness and deployment readiness, we incorporate constraint-based controls and safety checks across the generation pipeline. Our results demonstrate the feasibility of real-time, context-aware music generation in automotive settings, providing a foundation for personalized and adaptive in-vehicle audio experiences.
Cosmin Dragoiu, Nooshin Nabizadeh
Mercedes-Benz Research & Development North America
In Audio-Visual Navigation (AVN), agents must locate sound sources in unseen 3D environments using visual and auditory cues. However, existing methods often struggle with generalization in unseen scenarios, as they tend to overfit to semantic sound features and specific training environments. To address these challenges, we propose the Binaural Difference Attention with Action Transition Prediction (BDATP) framework, which jointly optimizes perception and policy. Specifically, the Binaural Difference Attention (BDA) module explicitly models interaural differences to enhance spatial orientation, reducing reliance on semantic categories. Simultaneously, the Action Transition Prediction (ATP) task introduces an auxiliary action prediction objective as a regularization term, mitigating environment-specific overfitting. Extensive experiments on the Replica and Matterport3D datasets demonstrate that BDATP can be seamlessly integrated into various mainstream baselines, yielding consistent and significant performance gains. Notably, our framework achieves state-of-the-art Success Rates across most settings, with a remarkable absolute improvement of up to 21.6 percentage points in Replica dataset for unheard sounds. These results underscore BDATP's superior generalization capability and its robustness across diverse navigation architectures.
Jia Li, Yinfeng Yu
Joint Research Laboratory for Embodied Intelligence, Xinjiang University · Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University · School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Audio-visual Navigation refers to an agent utilizing visual and auditory information in complex 3D environments to accomplish target localization and path planning, thereby achieving autonomous navigation. The core challenge of this task lies in the following: how the agent can break free from the dependence on training data and achieve autonomous navigation with good generalization performance when facing changes in environments and sound sources. To address this challenge, we propose an Audio Spatially-Guided Fusion for Audio-Visual Navigation method. First, we design an audio spatial feature encoder, which adaptively extracts target-related spatial state information through an audio intensity attention mechanism; based on this, we introduce an Audio Spatial State Guided Fusion (ASGF) to achieve dynamic alignment and adaptive fusion of multimodal features, effectively alleviating noise interference caused by perceptual uncertainty. Experimental results on the Replica and Matterport3D datasets indicate that our method is particularly effective on unheard tasks, demonstrating improved generalization under unknown sound source distributions.
Xinyu Zhou, Yinfeng Yu
Joint Research Laboratory for Embodied Intelligence, Xinjiang University · Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University · School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China