🤖 AI Summary
This work proposes a real-time, context-aware music generation method leveraging multimodal driving signals—such as dashcam imagery and vehicle telemetry—to enhance driver experience, attention, and well-being. By jointly modeling scene semantics and driving context, the system maps dynamic driving states to high-level musical attributes, which condition a generative audio model to synthesize low-latency, context-aligned soundtracks. The approach establishes the first end-to-end mapping from visual and motion inputs to structured musical features, enabling smooth transitions in response to evolving driving conditions. Integrated constraint-based controls and safety mechanisms ensure reliable deployment in automotive environments. Experimental results demonstrate the feasibility of real-time, adaptive audio generation within authentic in-vehicle settings, laying the groundwork for personalized intelligent cabin experiences.
📝 Abstract
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.