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This paper introduces Drive-to-Music, a context-aware system that generates real-time music tailored to driving conditions by leveraging multimodal inputs such as dashcam imagery and vehicle telemetry. By extracting scene semantics and driving context, the system maps these elements to musical descriptors, enabling the synthesis of audio that adapts dynamically to the driving environment. The results validate the feasibility of this approach, highlighting its potential to enhance driver experience and well-being through personalized soundtracks.
Real-time, context-aware music generation can transform in-vehicle experiences by adapting soundtracks to evolving driving conditions.
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.
