Comma2k19 LD

该数据集是CVPR2022论文"Towards Driving-Oriented Metric for Lane Detection Models"的数据集。为了在同一数据集上评估传统指标和以任务为中心的下游指标 E2E-LD 和 PSLD，需要用到车道线注释和驾驶信息（例如位置、转向角和速度）。 因此，我们创建了一个名为 Comma2k19-LD 的新数据集，我们在其中手动注释了 2,000 帧的左右车道线（100 个 20 Hz 的 1 秒剪辑场景）。 所选场景是从原始 Comma2k19 数据集中时速超过 30 mph（约 48 km/h）的场景中随机选择的。 请注意，原始 Comma2k19 数据集是在 MIT 许可下提供的。

[CVPR 2022] Towards Driving-Oriented Metric for Lane Detection Models

Explore and run machine learning code with Kaggle Notebooks | Using data from Comma2k19 LD

Maps are useful. Accurate maps are even more useful. Building accurate maps requires a lot of data. Mapping vehicles are expensive, EONs are not.

Comma2k19 LD Description: To evaluate both the conventional metrics and the downstream task-centric metrics E2E-LD and PSLD on the same dataset, we need both lane line annotations and driving information (e.g., position, steering angle, ...

comma 2k19 is a dataset of over 33 hours of commute in California's 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU.