An infotainment system of a vehicle (e.g., an automobile, a motorcycle, a bus, a recreational vehicle (RV), a semi-trailer truck, etc.) is configured to automatically switch from an Internet radio application (e.g., a music streaming application) to a radio station of a traditional radio (e.g., an AM/FM radio built into the vehicle) in certain situations, such as when the Internet is unavailable. In examples, the infotainment system uses machine learning to generate a personalized model of the music preferences of a user (e.g., a driver). Responsive to determining that a connection to the Internet (e.g., via communication components of the vehicle) is unreliable, poor, nonexistent, and/or the like, the infotainment system scans radio channels of the traditional radio for a radio station that is broadcasting relevant media content (e.g., media content predicted or otherwise determined to be desirable to the user based on the personalized model). Responsive to identifying a radio station broadcasting relevant media content, the infotainment system automatically stops playing the media content from the Internet radio application and starts playing relevant media content that is broadcast by the radio station. Thus, rather than the driver manually switching from the Internet radio to the traditional radio and then trying to find a radio station that is broadcasting relevant media content, which may be distracting to the driver, the techniques described herein automate such tasks. In this way, the techniques described may improve the user experience by reducing distractions to the driver while the driver is operating the vehicle, thereby potentially promoting driving safety and improving the driving experience.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.