Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
Authors: Penghao Wu, Li Chen, Hongyang Li, Xiaosong Jia, Junchi Yan, Yu Qiao
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. |
| Researcher Affiliation | Collaboration | 1Shanghai AI Laboratory 2University of California at San Diego 3Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/Open Drive Lab/PPGeo |
| Open Datasets | Yes | All pre-training experiments are conducted on the hours-long unlabeled You Tube driving videos (Zhang et al., 2022b). CARLA (Dosovitskiy et al., 2017). nu Scenes (Caesar et al., 2020). |
| Dataset Splits | Yes | We use the official train-val split for training and evaluation. We use different sizes of training data (from 4K to 40K) following Zhang et al. (2022b) to evaluate the generalization ability of pre-trained visual encoders when labeled data is limited and conduct the closed-loop evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and optimizers (e.g., Adam, AdamW, Torchvision) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the first stage in PPGeo pipeline, we train the model for 30 epochs by Adam (Kingma & Ba, 2015) optimizer with a learning rate of 10 4 which drops to 10 5 after 25 epochs. For the second stage, the encoder is trained for 20 epochs using the Adam W (Loshchilov & Hutter, 2017) optimizer. A cyclic learning rate scheduler is applied with the learning rate ranging from 10 6 to 10 4. The batch size for both stages is 128. We use data augmentations including Color Jitter, Ramdom Gray Scale, and Gaussian Blur. |