Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Model-Based Imitation Learning for Urban Driving
Authors: Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zachary Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | MILE improves upon prior state-of-the-art by 31% in driving score on the CARLA simulator when deployed in a completely new town and new weather conditions. |
| Researcher Affiliation | Collaboration | Anthony Hu1,2 Gianluca Corrado1 Nicolas Griffiths1 Zak Murez1 Corina Gurau1 Hudson Yeo1 Alex Kendall1 Roberto Cipolla2 Jamie Shotton1 1Wayve, UK. 2University of Cambridge, UK. |
| Pseudocode | No | The paper describes its architecture and models using mathematical formulations and textual descriptions, and refers to Appendix C for network details, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and model weights are available at https://github.com/wayveai/mile. |
| Open Datasets | No | The training data was collected in the CARLA simulator with an expert reinforcement learning (RL) agent [9] that was trained using privileged information as input... We collect data at 25Hz in four different training towns (Town01, Town03, Town04, Town06) and four weather conditions... for a total of 2.9M frames, or 32 hours of driving data. While the CARLA simulator [8] is publicly available, the specific dataset collected by the authors for this paper is not provided with a direct link or public repository. |
| Dataset Splits | No | The paper describes its training data (2.9M frames) and testing environment (Town05), but does not explicitly detail a separate validation set split or its purpose during training. |
| Hardware Specification | Yes | Our model was trained for 50, 000 iterations on a batch size of 64 on 8 V100 GPUs |
| Software Dependencies | Yes | All experiments were performed on CARLA 0.9.10, an open-source urban driving simulator [8], which comes with pre-built maps [52]. |
| Experiment Setup | Yes | Our model was trained for 50, 000 iterations on a batch size of 64 on 8 V100 GPUs, with training sequence length T = 12. We used the Adam W optimiser [44] with learning rate 10 4 and weight decay 0.01. |