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Argoverse dataset

Argoverse Dataset Papers With Cod

Argoverse Dataset | Papers With Code. Argoverse is a tracking benchmark with over 30K scenarios collected in Pittsburgh and Miami. Each scenario is a sequence of frames sampled at 10 HZ. Each sequence has an interesting object called agent, and the task is to predict the future locations of agents in a 3 seconds future horizon 3) Download Argoverse-Tracking and Argoverse-Forecasting. We provide both the full dataset and the sample version of the dataset for testing purposes. Head to our website to see the download option. Argoverse-Tracking provides track annotations, egovehicle poses, and undistorted, raw data from camera (@30hz) and lidar sensors (@10hz) as well as. What is Argoverse? One dataset with 3D tracking annotations for 113 scenes. One dataset with 324,557 interesting vehicle trajectories extracted from over 1000 driving hours. Two high-definition (HD) maps with lane centerlines, traffic direction, ground height, and more. One API to connect the map data with sensor information

Argoverse Dataset Collection • 2 roof-mounted LiDAR sensors • Overlapping 40° vertical field of view • Range of 200m • LiDAR sensors produce a point cloud with ~ 107,000 points at 10 Hz • Seven high-resolution ring cameras (1920 x 1200) recording at 30 Hz with a combined 360° field of view • Two front-view facing stere Argoverse is a research collection with three distinct types of data. The first is a dataset with sensor data from 113 scenes observed by our fleet, with 3D tracking annotations on all objects 3) Download Argoverse-Tracking and Argoverse-Forecasting. We provide both the full dataset and the sample version of the dataset for testing purposes. Head to our website to see the download option. Argoverse-Tracking provides track annotations and raw data from camera (@30hz) and lidar sensors (@10hz) as well as two stereo cameras (@5hz)

Official GitHub repository for Argoverse datase

  1. imal inter-vehicle interac-tions in the data. 3. The Argoverse Dataset Our sensor data, maps, and annotations are the primary contribution of this work. We also develop an API which helps connect the map data with sensor information e.g. ground point removal, nearest centerline queries, and lan
  2. ute long on average. We adopt a bottom-up approach for discovering a large vocabulary of 833 categories, an order of magnitude more than prior tracking benchmarks
  3. g evaluation that we name Argoverse-HD (High-frame-rate Detection). Despite being created for strea

Inside Argoverse: The Technical Details. Argoverse is a research collection with three distinct types of data. The first is a dataset with sensor data from 113 scenes observed by our fleet, with 3D tracking annotations on all objects Inspired by the KITTI dataset, Argoverse includes one dataset with 3D tracking annotations for 113 scenes and one dataset with 327,793 interesting vehicle trajectories extracted from over 1,000 driving hours. The Argoverse data collection also includes an API to connect sensor data with the HD map representation We present Argoverse, a dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps. The sensor data consists of 360 degree images from 7. Argoverse Dataset. The Argoverse dataset includes 3D tracking annotations for 113 scenes and over 324,000 unique vehicle trajectories for motion forecasting. 4. Berkeley DeepDrive Dataset. Also known as BDD 100K, the DeepDrive dataset gives users access to 100,000 annotated videos and 10 tasks to evaluate image recognition algorithms for. You use the Argoverse dataset to further your research and publish results based on the Argoverse benchmark. Your published paper includes imagery and figures derived from the Argoverse dataset. The imagery and figures are attributed to Argo and a hyperlink to the text of the CC-BY-NC-SA 4.0 license is provided

The Argoverse Motion Forecasting dataset includes more than 300,000 5-second tracked scenarios with a particular vehicle identified for trajectory forecasting. Argoverse is the first autonomous vehicle dataset to include HD maps with 290 km of mapped lanes with geometric and semantic metadata 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. Argoverse is the first large-scale autonomous driving dataset with such detailed maps. We investigate the potential utility of these new map features on two tasks - 3D tracking and motion forecasting, and we offer a significant amount of real-world, annotated data to enable new benchmarks for these problems

Graviti Open Datasets/Argoverse - Gravit

  1. Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges Di Feng*, Christian Haase-Schuetz*, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck and Klaus Dietmaye
  2. Argoverse™ is a dataset of high-definition maps and sensor data from Argo AI. In 2019, we released this collection publicly to aid the research community in making advancements in key perception and forecasting tasks for self-driving technology
  3. 1. Dataset Statistics In this section, we introduce the statistics of the HD map datasets in Table1. In the plain graph setting, we have a maximum of 250 nodes (control points) for the Argoverse dataset and 498 nodes for our In-house dataset. After rep-resenting the map data as hierarchical graphs, we conver

Introducing Argoverse: data and HD maps for computer

Dataset. We evaluate our method on two popular open dataset provided by two industry leaders: Waymo and Argoverse . Waymo 3D tracking dataset contains 800 training segments, 202 validation segments and 150 testing segments. Each segment includes ∼ 200 frames covering 20 seconds Because there is no traffic sign labels in argoverse HD map, we use Mapillary Traffic Sign Dataset to train the traffic sign detection network. However, the number of interested traffic signs in Mapillary dataset is not enough for our usage, so we build a synthetic traffic sign dataset by arbitrarily pasting random traffic signs on the images We analyzed the risk levels of different regions of Pittsburgh City based on the Argoverse Dataset. The risk level is identified as the clustered scenario number. Each scenario is a cluster learned by the DPGP model. Our results mainly include the traffic scenario clustering method, the learned scenarios and the visualization website Description We provide a dataset of dense and heterogeneous traffic videos. The dataset consists of the following road-agent categories - car, bus, truck, rickshaw, pedestrian, scooter, motorcycle, and other roadagents such as carts and animals. Overall, the dataset contains approximately 13 motorized vehicles, 5 pedestrians and 2 bicycles per frame, respectively

Introduction to Argoverse. Argoverse is part of a few datasets for self-driving vehicle R&D that have been published recently.It was recorded in Miami (204 km) and Pittsburgh (86 km) and can be divided into two subsets. Subset one contains 327793 vehicle trajectories (csv files) and subset two contains 113 scenes of 3D tracking annotations, 3D point clouds from a long-range LiDAR (10 Hz sample. This dataset is built for streaming object detection, for more details please check out the dataset webpage.. Competition. The competition on this dataset is hosted on Eval.AI, enter the challenge to win prizes and present at CVPR 2021 Workshop on Autonomous Driving

GitHub - yzhou377/argoverse-api: Official GitHub

Argoverse Gives Researchers Access to New Datasets for Autonomous Vehicles Thu, 08/08/2019 Developing autonomous vehicles has long been a hot topic in pop culture and the tech community, but the material that's needed to further academic research — data from autonomous vehicle sensors and other telemetry -- is usually kept under lock and key The Argoverse API provides useful functionality to interact with the 3 main components of our dataset: the HD Map, the Argoverse Tracking Dataset and the Argoverse Forecasting Dataset. from argoverse.map_representation.map_api import ArgoverseMap from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader from argoverse. The Argoverse dataset claims to be the first large-scale dataset with highly curated datasets and high-definition maps from over 1000 hours of driving data. The dataset contains two HD maps with geometric and semantic metadata such as lane centerlines, lane direction, and driveable area

argoverse-api. CVPR 2019, Official Devkit for the Argoverse Datasets. waymo_to_argoverse. Converts the Waymo Open Dataset to Argoverse Dataset format. nuscenes_to_argoverse. Converts the nuScenes Dataset to Argoverse Dataset format. argoverse_cbgs_kf_tracker. #1 entry on the Argoverse 3d Tracking Leaderboard, April 2020 - May 2020 The most relevant existing open dataset is the Argoverse Forecasting dataset . providing 300 hours of perception data and a lightweight HD semantic map encoding lane center positions. Our dataset differs in three substantial ways: 1) Instead of focusing on a wide city area we provide 1000 hours of data along a single route Detection and tracking on Argoverse Dataset

The nuScenes dataset is a large-scale autonomous driving dataset with 3d object annotations. It features: Full sensor suite (1x LIDAR, 5x RADAR, 6x camera, IMU, GPS) 1000 scenes of 20s each. 1,400,000 camera images. 390,000 lidar sweeps. Two diverse cities: Boston and Singapore Argoverse dataset. II. RELATED WORK a) Parametric scene understanding: Parametric scene understanding is the task of approximating a road scene with a set of parameters, that are often human interpretable and thus intuitive to use for downstream navigational reasoning or decision making tasks. Ess et. al. [2] propose a metho import argoverse: import shutil: from tqdm import tqdm: from argoverse. data_loading. argoverse_tracking_loader import ArgoverseTrackingLoader: import argoverse. visualization. visualization_utils as viz_util: from scipy. spatial. transform import Rotation ''' To get the internsity and ring information from argoverse data just attach this code. Dataset For this challenge, we use Argoverse-HD , a dataset that contains RGB video sequences from the autonomous driving dataset Argoverse 1.1 (only the front ring camera), and our own dense 2D bounding box annotations (1,250,000 boxes in total)

It also obtains state-of-the-art performance on the Argoverse dataset. Related Material @InProceedings{Gao_2020_CVPR, author = {Gao, Jiyang and Sun, Chen and Zhao, Hang and Shen, Yi and Anguelov, Dragomir and Li, Congcong and Schmid, Cordelia}, title = {VectorNet: Encoding HD Maps and Agent Dynamics From Vectorized Representation},. Argoverse forecasting dataset provides trajectory histories, context agents and lane centerline for future trajectory prediction. There are 333K 5-second long sequences in the dataset. The trajectories are sampled at 10Hz, with (0, 2] seconds for observation and (2, 5] seconds for future prediction

Experiments on the Argoverse dataset and an in-house dataset show that HDMapGen significantly outperforms baseline methods. Additionally, we demonstrate that HDMapGen achieves high scalability and efficiency. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as:. Other organizations that released open datasets in 2019 include Aptiv (previously Nutonomy) with the nuScenes dataset, Argo with the Argoverse dataset and Lyft with their Level 5 Dataset. In 2018, Berkeley A.I. Research (B.A.I.R), released the BDD100K dataset which is the largest to date in terms of monocular video data frames (120 million. Advanced Open Tools and Datasets for Autonomous Driving. ApolloScape , part of the Apollo project for autonomous driving, is a research-oriented project to foster innovations in all aspects of autonomous driving, from perception, navigation, to control. It hosts open access to semantically annotated (pixel-level) street view images and. Argoverse. The Argoverse dataset [7] is collected around Miami and Pittsburgh, USA in multiple weathers and dur-ing different times of a day. It provides images from stereo cameras and another seven cameras that cover 360 infor-mation. It also provides 64-beam LiDAR point clouds cap-tured by two 32-beam Velodyne LiDAR sensors stacked vertically We render N consecutive past frames, where N is 10 for the in-house dataset and 20 for the Argoverse dataset. Each frame is a 400 × 400 × 3 image, which has road map information and the detected object bounding boxes. 400 pixels correspond to 100 meters in the in-house dataset, and 130 meters in the Argoverse dataset. Rendering is based on.

The Argoverse dataset is representative of real-world conditions for LiDAR capture, and provides a challenging test environment for the networks trained on synthetic data. 6.1 Procedure. We collected data from 15 different vehicle tracks in Argoverse for which we have access to the ground truth CAD model in our synthetic dataset, ensuring that. Chang et al. proposed the Argoverse data set, which is designed to support self-driving vehicle perception data including 3D tracking and motion prediction. Argoverse data set includes sensor data collected by self-driving teams in Pittsburgh and Miami, as well as 3D tracking notes, extracting 300000 vehicle tracks and rich semantic maps It also outperforms the state of the art on the Argoverse dataset. Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e.g. pedestrians and vehicles) and road context information (e.g. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state.

Dataset [6] groups scenes recorded by multiple sensors, in-cluding a thermal imaging camera, by time slot, such as daytime, nighttime, dusk, and dawn. The Honda Research Institute 3D Dataset (H3D) [19] is a 3D object detection and tracking dataset that provides 3D LiDAR sensor readings recorded in 160 crowded urban scenes The method enables automatic dataset creation for this task from large-scale driving data. Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228,000 action sequences. Additionally, 2,245 action sequences were manually annotated for testing Datasets released in 2019 by Aptiv, Argo, Lyft and Waymo have started to incorporate multi-modal data from other sensors such as LiDAR, radar and stereo cameras. BDD100K was released in June 2018 and while it lacks the multi-modal data of its newer counterparts, it is the largest dataset based on monocular videos with 120 million image frames. A select group of key industry players already understand the importance of this balance to some extent, which is why, for example, Argo AI opted to curate its Argoverse datasets. While the open. What is Argoverse? - A set of three datasets designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting. - One dataset with 3D tracking annotations for 113 scenes - One dataset with 324,557 unusual vehicle trajectories extracted from over 1000 driving hours (motion forecasting

Waymo is releasing a massive trove of data from its autonomous vehicles for the research community. This motion dataset includes 570 hours of unique data from the company's vehicles captured. Waymo is also launching an open dataset challenge to encourage research teams in their work on behavior and prediction. There are four challenges: motion prediction, interaction prediction, real-time 3D detection, and real-time 2D detection. The winner of each challenge will receive $15,000, with second-place teams receiving $5,000 and third.

The training data comes from the multimodal dataset of the Argoverse dataset and nuScenes dataset, which has both map data and 3D object detection ground truth. PyrOccNet uses Bayesian Filtering to fuse the information across multiple cameras and across time in a coherent manner Driving datasets have attracted a lot of attention in recent years. KITTI [16], Cityscapes [10], Oxford RobotCar [34], BDD100K [57], NuScenes [6], and Argoverse [7] provide well annotated ground truth for visual odometry, stereo re-construction, optical flow, scene flow, object detection and tracking INTERPRET Sinlge Agent Prediction in the ICAPS21/ICCV21 Stage. In this part, the input is M agents' motion information including coordinates, velocities, yaw, vehicle length and width in the observed 1 second (10 frames) as well as the cooresponding HD map. The target is to predict one target agents' coordinates and yaw in the future 3 seconds.

Interacting Multiple Models(IMM) for Prediction Initializing search Home Coding Deep Learning Math Self Drivin The dataset for the Vehicle Motion Prediction task was collected by the Yandex Self-Driving Group (SDG) fleet. This is the largest vehicle motion prediction dataset released to date, containing 600,000 scenes (see creftype 8 for a comparison to other public datasets). We release this dataset under the CC BY NC SA 4.0 license 3) Download Argoverse-Tracking and Argoverse-Forecasting¶ We provide both the full dataset and the sample version of the dataset for testing purposes. Head to our website to see the download option. Argoverse-Tracking provides track annotations and raw data from camera (@30hz) and lidar sensors (@10hz) as well as two stereo cameras (@5hz). We. Argoverse数据集是由Argo AI、卡内基梅隆大学、佐治亚理工学院发布的用于支持自动驾驶汽车3D Tracking和Motion Forecasting研究的数据集。数据集包括两个部分:Argoverse 3D Tracking与Argoverse Motion Forecasti 写文章. 自动驾驶数据集-Argoverse Dataset

Argoverse Dataset 自动驾驶数据集 Argoverse运动预测是一个精心挑选的324,557个场景集合,每个场景5秒,用于训练和验证。每个场景都包含以10 Hz采样的每个跟踪对象的2D鸟瞰质心。 为了创建这个集合,我们从自动驾驶测试车队中筛选了1000多个小时的驾驶数据,以. segmentation. Likewise, Argoverse [8] dataset provides map annotation for improving perception algorithms but cannot be used for algorithms requiring Radar data as provided by nuScenes [7]. To innovate perception systems that require diverse sensor modalities or methods that integrate multiple perception tasks, existing datasets might not be. Argoverse is a research collection with three distinct types of data. The first is a dataset with sensor data from 113 scenes observed by our fleet, with 3D tracking annotations on all objects. The second is a dataset of 300,000-plus scenarios observed by our fleet, wherein each scenario contains motion trajectories of all observed objects. Earlier this year, we released Argoverse, a curated collection of high-definition maps and sensor data from a fleet of Argo AI self-driving test vehicles.Today, we're launching the first two Argoverse competitions using our motion forecasting and 3D tracking datasets, and inviting academic researchers and students to participate.. To support the competitions, we're opening two evaluation. The experiments conducted on the public nuScenes dataset and Argoverse dataset demonstrate that the proposed LaPred method significantly outperforms the existing prediction models, achieving state-of-the-art performance in the benchmarks. Comments: 13 pages, 2 figures, 7 tables, CVPR 2021

Select a dataset to load from the drop down box below. CMRNet++ is trained on both KITTI and Argoverse datasets. All examples depict places that were never seen during the training phase. Note that the Lyft5 dataset was not included in the training set. We evaluate the generalization ability of CMRNet++ on Lyft5 without any retraining A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories periments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on mo-tion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://decisionforce. github.io/mmTransformer. 1. Introductio

TAO Datase

Alphabet's autonomous driving unit, Waymo, recently surprised the industry by releasing its Waymo Open Dataset, following Argo AI's release of its Argoverse dataset and Lyft's Level 5 dataset. Waymo's Open Dataset. Like other autonomous driving technology companies, Waymo too has built its Open Dataset on a foundation of rigorous testing Datasets. In the paper, we've presented results for KITTI 3Dobject, KITTI Odometry, KITTI RAW and Argoverse 3D Tracking v1.0 datasets. For comparision with Schulter et. al., We've used the same training and test splits sequences from the KITTI RAW dataset. For more details about the training/testing splits one can look at the splits directory Argoverse also contains datasets to train 3D tracking models that detect and follow objects like vehicles and anything else within a road's potential drivable area. There are 113 segments. Traffic Data. highD dataset: new dataset of naturalistic vehicle trajectories recorded on German highways, using a drone https://www.highd-dataset.com/; Open Data. First of all, thank you very much for a large amount of processed data. There are no problems for research purposes, but there are some questions about the dataset in competition. In test dataset 2, 13, 75, etc., we can see that the time difference between frames is not constant. In the paper, the time difference between frames of the dataset is 10hz, but the difference between the actual.

Argoverse API — argoverse documentation

Streaming Perception - Carnegie Mellon Universit

Our experiments on simulation, KITTI, and Argoverse datasets show that our 3D tracking pipeline offers robust data association and tracking. On Argoverse, our image-based method is significantly better for tracking 3D vehicles within 30 meters than the LiDAR-centric baseline methods lier datasets collected data in the frontal direction only, ig- noring objects to the sides or rear that are also important to decision-making in driving, Argoverse [13], Audi [18] 05.04.2012: Added links to the most relevant related datasets and benchmarks for each category. 04.04.2012: Our CVPR 2012 paper is available for download now! 20.03.2012: The KITTI Vision Benchmark Suite goes online, starting with the stereo, flow and odometry benchmarks KITTI is one of the well known benchmarks for 3D Object detection. Working with this dataset requires some understanding of what the different files and their contents are. Goal here is to do som

自动驾驶数据集汇总-至2019 - 知乎

5 we were using a trajectory bank built from the in-house dataset in the evaluation of our method on the Argoverse dataset. 6 We've fixed the mistake and re-evaluated the proposed method. The updated results can be seen in Table 1 As the first step, the Argoverse tracking dataset which is collected by Argo AI in Pittsburgh local roads is used to test the performance of the proposed unsupervised kernelmethods. It contains data recorded by one lidar (10Hz) and multiple cameras equipped on the ego vehicle including the relative positions and bounding boxes ofobjectsdetected Sep, 2019: We are very excited to launch the first two Argoverse competitions for motion forecasting and 3D tracking.Winners will be announced at NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving on December 14, 2019.We also released Argoverse dataset 1.1 with a lot of improvement Using datasets from Pittsburgh city streets, researchers analyzed and identified roads where collisions are most likely to occur and share results with automated vehicle (AV) manufacturers to facilitate a successful AV deployment. Approach: The project team used the Argoverse Tracking Dataset, a public data set maintained by Argo AI

Argoverse Gives Researchers Access to New Datasets for

In this release, we have extended Open3D-ML with the task of 3D object detection. This extension introduces support for new datasets, such as the Waymo Open dataset, Lyft level 5 open data, Argoverse, nuScenes, and KITTI. As always, all these datasets can be visualized out-of-the-box using our visualization tool, from Python or C++ the Argoverse, Lyft, and Apolloscape datasets and highlight the benefits over prior trajectory prediction methods. In practice, our approach reduces the average prediction er-ror by more than 54% over prior algorithms and achieves a weighted average accuracy of 91.2% for behavior predic-tion. 1. Introductio CVPR 2020 Argoverse competition (honorable mention award) 23. Towards Debiasing Sentence Representations Paul Pu Liang, Irene Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency ACL 2020 [code] 22. On Emergent Communication in Competitive Multi-Agent Team the Argoverse-HD dataset, have a sweet spot at 300 proposals and scale=640. This is likely due to the difference in the domain, as ImageNet-VID has fewer number of objects in average compared to Argoverse-HD. Thus, identifying the optimal configurations at run-time is non-trivial. Figure2shows that there exist numerous tradeof John Lambert Page 3 ACADEMIC MSeg: A Composite Dataset for Multi-domain Semantic Segmentation TALKS Invited Speaker,ROS World 2020Conference, Recent Advances in Robotic Perception, November 2020. Invited Panelist Talk, Robust Vision Challenge Workshop, ECCV 2020, October 2020.[Video] Argoverse: 3D Tracking and Forecasting with Rich Map

We demonstrate the effectiveness of our approach by evaluating against several challenging baselines on the NuScenes and Argoverse datasets, and show that we are able to achieve a relative improvement of 9.1% and 22.3% respectively compared to the best-performing existing method 3D object detection is a core perceptual challenge for robotics and autonomous driving. However, the class-taxonomies in modern autonomous driving datasets are significantly smaller than many influential 2D detection datasets. In this work, we address the long-tail problem by leveraging both the large class-taxonomies of modern 2D datasets and the robustness of state-of-the-art 2D.

Argoverse: 3D Tracking and Forecasting With Rich Maps

Thus this framework effectively mitigates the complexity of motion prediction problem while ensuring the multimodal output. Experiments on four large-scale trajectory prediction datasets, i.e. the ETH, UCY, Apollo and Argoverse datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively The superior experimental results of our PMBM tracker on public Waymo and Argoverse datasets clearly illustrate that an RFS-based tracker outperforms many state-of-the-art deep learning-based and Kalman filter-based methods, and consequently, these results indicate a great potential for further exploration of RFS-based frameworks for 3D MOT. Our experiments on simulation, KITTI, and Argoverse datasets show that our 3D tracking pipeline offers robust data association and tracking. On Argoverse, our image-based method is significantly better for tracking 3D vehicles within 30 meters than the LiDAR-centric baseline methods. read mor For instance, Argoverse also has a motion forecasting data set with 3D tracking annotation for 113 scenes and more than 300,000 vehicle trajectories, including unprotected left turns and lane. AV / Self-Driving Car Datasets I replied to a comment about AV data sharing and I started to build a list of sources for AV Data so I thought I would share it. This is just a snapshot of what is out there, please add to this list

Best Open-Source Autonomous Driving Datasets — SiaSearc

  1. BDD100K Dataset from Berkeley DeepDrive. BDD100K dataset is a large collection of 100K driving videos with diverse scene types and weather conditions. Along with the video data, we also released annotation of different levels on 100K keyframes, including image tagging, object detection, instance segmentation, driving area and lane marking
  2. The goal of your project is to try something new and, perhaps, to contribute something to the field of robot learning. Projects should be done in groups of one to three people. Feel free to use the Search for Teammates topic on Piazza to form teams. There is also a project tag for discussing project ideas. And of course, you're encouraged to discuss possible topics with us during office hours
  3. Network Architecture: We show the trajectory and behavior prediction for the ith road-agent (red circle in the traffic-graphs). The input consists of the spatial coordinates over the past T seconds as well as the eigenvectors (green rectangles, each shade of green represents the index of the eigenvectors) of the traffic-graphs corresponding to the first T traffic-graphs
  4. CMU Argo AI Center for Autonomous Vehicle Research. Carnegie Mellon is the birthplace of autonomous vehicles (AVs) and has been a pioneering leader in AV technologies since the 1980s. The collaboration between CMU and Argo AI will support the development of advanced autonomous capabilities that will enable large-scale deployment of self-driving.
  5. The dataset, collected during winter within the Region of Waterloo, Canada, is the first autonomous driving dataset that focuses on adverse driving conditions specifically. It contains 7,000 frames of annotated data from 8 cameras (Ximea MQ013CG-E2), lidar (VLP-32C), and a GNSS+INS system (Novatel OEM638), collected through a variety of winter.
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Evaluation - EvalA

  1. Argo AI is releasing curated data along with high-definition maps to researchers for free, the latest company in the autonomous vehicle industry to open source some of the information it has captured while developing and testing self-driving cars. The aim, the Ford Motor-backed company says, is to give academic researchers the ability to study th
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  3. Argoverse: 3D Tracking and Forecasting with Rich Maps
  4. nuScenes Dataset Papers With Cod
  5. Argoverse: 3D Tracking and Forecasting with Rich Maps DeepA
  6. Workshop on Autonomous Driving - cvpr2021
Forecasting Trajectory and Behavior of Road-Agents Using

Argo AI And Waymo Release Automated Driving Data Set

  1. [2005.04259] VectorNet: Encoding HD Maps and Agent ..
  2. Datasets Deep Multi-modal Object Detection and Semantic
  3. Overview - EvalA
  4. Multi-Object Tracking using Poisson Multi-Bernoulli
Predicting Semantic Map Representations from Images using過去最大級の膨大な自動運転データセットを無償でWaymoが公開 - GIGAZINE