# Data ## Download The files mentioned below can also be downloaded via [OpenDataLab](https://opendatalab.com/OpenLane-V2/download). It is recommended to use provided [command line interface](https://opendatalab.com/OpenLane-V2/cli) for acceleration. | Subset | Split | Google Drive Google Drive | Baidu Yun Baidu Yun | md5 | Size | | --- | --- | --- | --- | --- | --- | | subset_A | sample |[sample](https://drive.google.com/file/d/1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6/view?usp=share_link) | [sample](https://pan.baidu.com/s/1ncqwDtuihKTBZROL5vdCAQ?pwd=psev) | 21c607fa5a1930275b7f1409b25042a0 | ~300M | | subset_A | all | [info](https://drive.google.com/file/d/1t47lNF4H3WhSsAqgsl9lSLIeO0p6n8p4/view?usp=share_link) | [info](https://pan.baidu.com/s/1uXpX4hqlMJLm0W6l12dJ-A?pwd=6rzj) |95bf28ccf22583d20434d75800be065d | ~8.8G | | | train | [image_0](https://drive.google.com/file/d/1jio4Gj3dNlXmSzebO6D7Uy5oz4EaTNTq/view?usp=share_link) | [image_0](https://pan.baidu.com/s/12aV4CoT8znEY12q4M8XFiw?pwd=m204) | 8ade7daeec1b64f8ab91a50c81d812f6 | ~14.0G | | | | [image_1](https://drive.google.com/file/d/1IgnvZ2UljL49AzNV6CGNGFLQo6tjNFJq/view?usp=share_link) | [image_1](https://pan.baidu.com/s/1SArnlA2_Om9o0xcGd6-EwA?pwd=khx8) | c78e776f79e2394d2d5d95b7b5985e0f | ~14.3G | | | | [image_2](https://drive.google.com/file/d/1ViEsK5hukjMGfOm_HrCiQPkGArWrT91o/view?usp=share_link) | [image_2](https://pan.baidu.com/s/1ZghG7gwJqFrGxCEcUffp8A?pwd=0xgm) | 4bf09079144aa54cb4dcd5ff6e00cf79 | ~14.2G | | | | [image_3](https://drive.google.com/file/d/1r3NYauV0JIghSmEihTxto0MMoyoh4waK/view?usp=share_link) | [image_3](https://pan.baidu.com/s/1ogwmXwS9u-B9nhtHlBTz5g?pwd=sqeg) | fd9e64345445975f462213b209632aee | ~14.4G | | | | [image_4](https://drive.google.com/file/d/1aBe5yxNBew11YRRu-srQNwc5OloyKP4r/view?usp=share_link) | [image_4](https://pan.baidu.com/s/1tMAmUcZH2SzCiJoxwgk87w?pwd=i1au) | ae07e48c88ea2c3f6afbdf5ff71e9821 | ~14.5G | | | | [image_5](https://drive.google.com/file/d/1Or-Nmsq4SU24KNe-cn9twVYVprYPUd_y/view?usp=share_link) | [image_5](https://pan.baidu.com/s/1sRyrhcSz-izW2U5x3UACSA?pwd=nzxx) | df62c1f6e6b3fb2a2a0868c78ab19c92 | ~14.2G | | | | [image_6](https://drive.google.com/file/d/1mSWU-2nMzCO5PGF7yF9scoPntWl7ItfZ/view?usp=share_link) | [image_6](https://pan.baidu.com/s/1P3zn_L6EIGUHb43qWOJYWg?pwd=4wei) | 7bff1ce30329235f8e0f25f6f6653b8f | ~14.4G | | | val | [image_7](https://drive.google.com/file/d/19N5q-zbjE2QWngAT9xfqgOR3DROTAln0/view?usp=share_link) | [image_7](https://pan.baidu.com/s/1rRkPWg-zG2ygsbMhwXjPKg?pwd=qsvb) | c73af4a7aef2692b96e4e00795120504 | ~21.0G | | | test | [image_8](https://drive.google.com/file/d/1CvT9w0q8vPldfaajI5YsAqM0ZINT1vJv/view?usp=share_link) | [image_8](https://pan.baidu.com/s/10zjKeuAw350fwTYAeuSLxg?pwd=99ch) | fb2f61e7309e0b48e2697e085a66a259 | ~21.2G | | subset_B | coming soon | - | - | - | - | For files in Google Drive, you can use the following command by replacing `[FILE_ID]` and `[FILE_NAME]` accordingly: ```sh wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=[FILE_ID]' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=[FILE_ID]" -O [FILE_NAME] ``` ## Preprocess The dataset is preprocessed into pickle files representing different collections, which then be used for training models or evaluation: ```sh cd data python OpenLane-V2/preprocess.py ``` ## Hierarchy The hierarchy of folder `OpenLane-V2/` is described below: ``` └── OpenLane-V2 ├── train | ├── [segment_id] | | ├── image | | | ├── [camera] | | | | ├── [timestamp].jpg | | | | └── ... | | | └── ... | | └── info | | ├── [timestamp].json | | └── ... | └── ... ├── val | └── ... ├── test | └── ... ├── data_dict_example.json ├── data_dict_subset_A.json ├── data_dict_subset_B.json ├── openlanev2.md5 └── preprocess.py ``` - `[segment_id]` specifies a sequence of frames, and `[timestamp]` specifies a single frame in a sequence. - `image/` contains images captured by various cameras, and `info/` contains meta data and annotations of a single frame. - `data_dict_[xxx].json` notes the split of train / val / test under the subset of data. ## Meta Data The json files under the `info/` folder contain meta data and annotations for each frame. Each file is formatted as follows: ``` { 'version': -- version 'segment_id': -- segment_id 'meta_data': { 'source': -- name of the original dataset 'source_id': -- original identifier of the segment } 'timestamp': -- timestamp of the frame 'sensor': { [camera]: { -- name of the camera 'image_path': -- image path 'extrinsic': -- extrinsic parameters of the camera 'intrinsic': -- intrinsic parameters of the camera }, ... } 'pose': -- ego pose 'annotation': -- anntations for the current frame } ``` ## Annotations For a single frame, annotations are formatted as follow: ``` { 'lane_centerline': [ (n lane centerlines in the current frame) { 'id': -- unique ID in the current frame 'points': [n, 3] -- 3D coordiate 'confidence': -- confidence, only for prediction }, ... ], 'traffic_element': [ (k traffic elements in the current frame) { 'id': -- unique ID in the current frame 'category': -- traffic element category 1: 'traffic_light', 2: 'road_sign', 'attribute': -- attribute of traffic element 0: 'unknown', 1: 'red', 2: 'green', 3: 'yellow', 4: 'go_straight', 5: 'turn_left', 6: 'turn_right', 7: 'no_left_turn', 8: 'no_right_turn', 9: 'u_turn', 10: 'no_u_turn', 11: 'slight_left', 12: 'slight_right', 'points': [2, 2] -- top-left and bottom-right corners of the 2D bounding box 'confidence': -- confidence, only for prediction }, ... ], 'topology_lclc': [n, n] -- adjacent matrix among lane centerlines 'topology_lcte': [n, k] -- adjacent matrix between lane centerlines and traffic elements } ``` - `id` is the identifier of a lane centerline or traffic element and is consistent in a sequence. For predictions, it can be randomly assigned but unique in a single frame. - `topology_lclc` and `topology_lcte` are adjacent matrices, where row and column are sorted according to the order of the lists `lane_centerline` and `traffic_element`. It is a MUST to keep the ordering the same for correct evaluation. For ground truth, only 0 or 1 is a valid boolean value for an element in the matrix. For predictions, the value varies from 0 to 1, representing the confidence of the predicted relationship. - #lane_centerline and #traffic_element are not required to be equal between ground truth and predictions. In the process of evaluation, a matching of ground truth and predictions is determined.