--- license: mit library_name: keras tags: - autonomous-driving - end-to-end - imitation-learning - self-driving - udacity - vision - cnn - dave2 - nvidia datasets: - maxim-igenbergs/thesis-data --- # DAVE-2 End-to-End Driving Model Implementation of NVIDIA's DAVE-2 architecture trained on the Udacity self-driving car simulator for the bachelor's thesis: Dual-Axis Testing of Visual Robustness and Topological Generalization in Vision-based End-to-End Driving Models. ## Model Description DAVE-2 is the original end-to-end driving architecture proposed by NVIDIA in 2016. It learns to map raw camera images directly to steering and throttle commands through imitation learning. ### Architecture ``` Input: RGB Image (66 × 200 × 3) ↓ 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 ↓ Flatten ↓ Dense(1164) + ELU Dense(100) + ELU Dense(50) + ELU Dense(10) + ELU ↓ Output: [steering, throttle] ``` ## Checkpoints | Map | Checkpoint | |-----|------------| | GenRoads | `genroads_20251028-145557/` | | Jungle | `jungle_20251209-175046/` | ### Files per Checkpoint - `best_model.h5`: Keras model weights - `meta.json`: Training configuration and hyperparameters - `history.csv`: Training/validation metrics per epoch - `loss_curve.png`: Visualization of training progress ## Citation ```bibtex @thesis{igenbergs2026dualaxis, title={Dual-Axis Testing of Visual Robustness and Topological Generalization in Vision-based End-to-End Driving Models}, author={Igenbergs, Maxim}, school={Technical University of Munich}, year={2026}, type={Bachelor's Thesis} } ``` ## Related - [DAVE-2-GRU Driving Model](https://huggingface.co/maxim-igenbergs/dave2-gru) - [ViT Driving Model](https://huggingface.co/maxim-igenbergs/vit) - [TCP Driving Model](https://huggingface.co/maxim-igenbergs/tcp-carla-repro) - [Training Data](https://huggingface.co/datasets/maxim-igenbergs/thesis-data) - [Evaluation Runs](https://huggingface.co/datasets/maxim-igenbergs/thesis-runs)