Two TFLite models used by
Car Nebula for real-time Advanced Driver
Assistance (ADAS) running entirely on-device on Android automotive hardware.
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Camera frame (CameraX / USB UVC)
│
├──► EfficientDet Lite0 (every 3 frames)
│ └──► AdasBox list: label, bounding rect, estimated distance
│ └──► HUD overlay: boxes, collision warning, top-down view
│
└──► UFLD ResNet-18 (every 3 frames)
└──► Lane boundary points (left/right, top/bottom)
└──► Camera overlay: seg mask, lane lines
HUD: road corridor, departure warning
Results are temporally smoothed between inference runs so the UI always has
something to render even on frames that skip inference.
1// Load from downloaded file
2val model = FileInputStream(file).channel.use { ch ->
3 ch.map(FileChannel.MapMode.READ_ONLY, 0, file.length())
4}
5val interpreter = InterpreterApi.create(
6 model,
7 InterpreterApi.Options()
8 .setRuntime(InterpreterApi.Options.TfLiteRuntime.FROM_SYSTEM_ONLY)
9 .setNumThreads(2)
10)
