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 | Conference PDF | Archive PDF | Plain Text | 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. |