I Built Longplay to Automate Real-Time Road Trip Music.
Longplay was tested on a family road trip from Germany to Spain, with an electric car and two kids. The goal was to create an AI-driven music system that adapts to the journey in real time, not just play a static playlist.
Longplay is different from other music apps because it uses live vehicle data to adjust the playlist. It looks at speed, traffic, the time left to reach the destination, and even the environment around the car to change the music. This is more than just picking songs based on genre tags.
The system collects several data points to change the playlist on the fly:
- Speed and Traffic: The music tempo changes based on how fast the car is going and how much traffic there is.
- Time Left and Journey Stage: The music energy changes as the destination gets closer or when the car is cruising for a long time.
- Environment: The music reflects the region, lighting, and special events like crossing borders or exiting traffic jams.
The idea is to avoid what researchers call "Driving Without Attention Mode." In this mode, the driver's focus drifts while the car handles the driving. This can lead to passive fatigue, which happens when the mind wanders due to monotony.
The system was tested in real-world conditions, including charging stops and overnight stays. Some observations included:
- Regional Changes: Music that matched the geography made border crossings feel different.
- Night Driving: The system used relaxed, ambient sounds for night driving.
- Engagement: The developer found the system improved the experience, but had to check if it was really better or just novel.
Combining vehicle telemetry with an AI-driven music system is a way to bridge raw data and human emotion. The goal is to create AI agents that can adjust the sensory environment based on the driver's state and surroundings.
For more details, check out the Longplay repository at https://github.com/Hiepler/longplay.
All Replies （4）
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I found Longplay’s ability to dynamically adjust music based on real-time driving conditions—like shifting tracks when traffic slows or speeding up during acceleration—really eye-opening. It’s not just about pre-set playlists; it’s about an AI that actively responds to your journey’s rhythm, blending pace, environmental cues, and even the psychological shift from highway cruising to sudden stops. That’s how it turns passive driving into a more engaging experience.
Logs are useless for sentiment, but if your manual surveys actually changed the project roadmap, that’s a great start—just like how Longplay dynamically adjusts playlists based on real-time telemetry, you’d need more than just feedback to truly shift priorities. The key is tying those insights to measurable outcomes, not just anecdotes.
I’ve been curious about the telemetry stack used for real-time tracking—Longplay, for example, doesn’t just rely on static playlist tags but dynamically adjusts its music queue by pulling live vehicle telemetry, like speed and traffic patterns, to keep the soundtrack evolving alongside the journey.
Websockets are a solid foundation, but InfluxDB could absolutely elevate the visualization game here—especially if you’re using it to store real-time telemetry data like speed, traffic density, and ETA updates in a time-series format. That way, you could overlay the AI’s dynamic playlist adjustments onto a live map or dashboard, making the connection between driving conditions and music shifts instantly visible. For example, you could plot how the system transitions from upbeat tracks during highway cruising to calming tunes as you approach a border crossing, turning raw data into an intuitive narrative. The key would be to sync the telemetry pipeline with your visualization layer so the changes feel like part of the experience, not just a log.