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Input: RGB Image (66 × 200 × 3)
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Conv2D(24, 5×5, stride=2) + ELU
Conv2D(36, 5×5, stride=2) + ELU
Conv2D(48, 5×5, stride=2) + ELU
Conv2D(64, 3×3) + ELU
Conv2D(64, 3×3) + ELU
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Flatten
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Dense(1164) + ELU
Dense(100) + ELU
Dense(50) + ELU
Dense(10) + ELU
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Output: [steering, throttle]| Map | Checkpoint |
|---|---|
| GenRoads | genroads_20251028-145557/ |
| Jungle | jungle_20251209-175046/ |
best_model.h5: Keras model weightsmeta.json: Training configuration and hyperparametershistory.csv: Training/validation metrics per epochloss_curve.png: Visualization of training progress1@thesis{igenbergs2026dualaxis,
2 title={Dual-Axis Testing of Visual Robustness and Topological Generalization in Vision-based End-to-End Driving Models},
3 author={Igenbergs, Maxim},
4 school={Technical University of Munich},
5 year={2026},
6 type={Bachelor's Thesis}
7}