The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers.
1,549 PAPERS • 20 BENCHMARKS
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in thi
359 PAPERS • 16 BENCHMARKS
CULane is a large scale challenging dataset for academic research on traffic lane detection. It is collected by cameras mounted on six different vehicles driven by different drivers in Beijing. More than 55 hours of videos were collected and 133,235 frames were extracted. The dataset is divided into 88880 images for training set, 9675 for validation set, and 34680 for test set. The test set is divided into normal and 8 challenging categories.
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The TuSimple dataset consists of 6,408 road images on US highways. The resolution of image is 1280×720. The dataset is composed of 3,626 for training, 358 for validation, and 2,782 for testing called the TuSimple test set of which the images are under different weather conditions.
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OpenLane is the first real-world and the largest scaled 3D lane dataset to date. The dataset collects valuable contents from public perception dataset Waymo Open Dataset and provides lane&closest-in-path object(CIPO) annotation for 1000 segments. In short, OpenLane owns 200K frames and over 880K carefully annotated lanes. The OpenLane Dataset is publicly released to aid the research community in making advancements in 3D perception and autonomous driving technology.
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CurveLanes is a new benchmark lane detection dataset with 150K lanes images for difficult scenarios such as curves and multi-lanes in traffic lane detection. It is collected in real urban and highway scenarios in multiple cities in China. It is the largest lane detection dataset so far and establishes a more challenging benchmark for the community.
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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.
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ONCE-3DLanes is a real-world autonomous driving dataset with lane layout annotation in 3D space. A dataset annotation pipeline is designed to automatically generate high-quality 3D lane locations from 2D lane annotations by exploiting the explicit relationship between point clouds and image pixels in 211,000 road scenes.
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The unsupervised Labeled Lane MArkerS dataset (LLAMAS) is a dataset for lane detection and segmentation. It contains over 100,000 annotated images, with annotations of over 100 meters at a resolution of 1276 x 717 pixels. The Unsupervised Llamas dataset was annotated by creating high definition maps for automated driving including lane markers based on Lidar.
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Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised Domain Adaptation has been applied to a wide variety of complex vision tasks, only few works focus on lane detection for autonomous driving. This can be attributed to the lack of publicly available datasets. To facilitate research in these directions, we propose CARLANE, a 3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE encompasses the single-target datasets MoLane and TuLane and the multi-target dataset MuLane. These datasets are built from three different domains, which cover diverse scenes and contain a total of 163K unique images, 118K of which are annotated. In addition we evaluate and report systematic baselines, including our own method, which builds upon Prototypical Cross-domain Self-supervised Learning. We find that false positive and false negative rates of the eva
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ELAS is a dataset for lane detection. It contains more than 20 different scenes (in more than 15,000 frames) and considers a variety of scenarios (urban road, highways, traffic, shadows, etc.). The dataset was manually annotated for several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).
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KAIST-Lane (K-Lane) is the world’s first and the largest public urban road and highway lane dataset for Lidar. K-Lane has more than 15K frames and contains annotations of up to six lanes under various road and traffic conditions, e.g., occluded roads of multiple occlusion levels, roads at day and night times, merging (converging and diverging) and curved lanes.
3 PAPERS • 1 BENCHMARK
VIL-100 is a video instance lane detection dataset, which contains 100 videos with in total 10,000 frames, acquired from different real traffic scenarios. All the frames in each video are manually annotated to a high-quality instance-level lane annotation, and a set of frame-level and video-level metrics are included for quantitative performance evaluation.
TuSimple Lane is an extension of the TuSimple dataset with 14,336 lane boundaries annotations. Each lane boundary in the dataset is annotated using 7 different classes such as “Single Dashed”, “Double Dashed” or “Single White Continuous”.
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DET is a lane detection dataset that consists of the raw event data, accumulated images over 30ms and corresponding lane labels. Contains 17,103 lane instances, each of which is labeled pixel by pixel manually.
1 PAPER • 1 BENCHMARK