metadata
license: mit
library_name: keras
tags:
- autonomous-driving
- end-to-end
- imitation-learning
- self-driving
- udacity
- vision
- cnn
- gru
- recurrent
- temporal
datasets:
- maxim-igenbergs/thesis-data
DAVE-2-GRU End-to-End Driving Model
DAVE-2 architecture extended with GRU (Gated Recurrent Unit) for temporal modeling, 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-GRU extends the original NVIDIA DAVE-2 architecture by adding a GRU layer to capture temporal dependencies across frames. This allows the model to leverage sequential information for smoother and more context-aware driving predictions.
Architecture
Input: RGB Image Sequence (N × 66 × 200 × 3)
↓
TimeDistributed:
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
↓
GRU(128)
↓
Dense(100) + ELU
Dense(50) + ELU
Dense(10) + ELU
↓
Output: [steering, throttle]
Checkpoints
| Map | Checkpoint |
|---|---|
| GenRoads | genroads_20251215-174930/ |
| Jungle | jungle_20251201-142321/ |
Files per Checkpoint
best_model.h5: Keras model weightsmeta.json: Training configuration and hyperparametershistory.csv: Training/validation metrics per epochloss_curve.png: Visualization of training progress
Citation
@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}
}