Head on over to www.argoverse.org/tasks to get started. Below is the description of the metrics we compute: The leaderboard can be sorted by any metric by clicking on the metric name in the column header. Use rasterized driveable area ROI to decimate LiDAR point cloud to only ROI points. Geek level 1000!!! Since she exploded on the scene with her two juicy, impossible-to-put-down tell-alls, readers have been dying to know: what makes Karrine Steffans tick? See more details in our CVPR paper. By “amodal” we mean that the 3D extent of each cuboid represents the spatial extent of the object in 3D space — and not simply the extent of observed pixels or observed LiDAR returns, which is smaller for occluded objects and ambiguous for objects seen from only one face. In Argoverse motion forecasting competition, the task is to predict the probabilistic future trajectory distribution for the interested targets in the traffic scene. All of the detail we provide makes it possible to develop more accurate perception algorithms, which in turn will enable self-driving vehicles to safely . 2. Retrieve most probable tangent vector ∈ℝ² to lane centerline. Runners-up will also receive awards. Argoverse Motion Forecasting Competition Organized by: argoai-argoverse Starts on: Sep 27, 2019 5:00:00 PM Open now through June 13. However, this is not just an individual experience, but a collective one, as you are able to find your own story written amongst these pages. We require one page or longer pdf reports to be emailed to argoverse-competition@argo.ai by the June 13th deadline so that we can . In addition, I will discuss the Argoverse dataset for autonomous driving. New! Argoverse is the first large-scale self-driving data collection to include HD maps with geometric and semantic metadata — such as lane centerlines, lane direction, and driveable area. This book offers new perspectives on Israel’s evolving Mediterranean identity, which centers around the longing to find a "natural" place in the region. Argoverse is a tracking benchmark with over 30K scenarios collected in Pittsburgh and Miami. Argoverse is the first large-scale self-driving data collection to include HD maps with geometric and semantic metadata — such as lane centerlines, lane direction, and driveable area. Therefore, our stereo dataset captures more scene details at the near and far range, crucial for autonomous vehicles. It’s going to take extraordinary effort from exceptional people to build safe and reliable self-driving vehicles that can anticipate and react to the actions of everyone and everything on the roads around us. Found insideBut early promises that these autonomous vehicles would soon be on the roads have proven premature. Alex Davies follows the twists and turns of the story from its origins to today. Found inside – Page iWith saucy detail on every page, this no-nonsense guide reveals why a strong woman is much more desirable than a “yes woman” who routinely sacrifices herself. The competition will end on June 10th. Second place winners will receive a cash prize of $500. The Argoverse lidar range is up to 200 meters, roughly twice the range of the sensor used in the KITTI dataset, and our lidar produces a point cloud with greater density at each sweep. Publicly available datasets for self-driving research rarely include rich map data, even though detailed maps are critical to the development of self-driving systems. Any road user’s journey coincides with hundreds or even thousands of others. This book unpacks the details of the practical achievements involved in socially engaging with people at high speed. Our leaderboards remain open for 3D Detection and 3D Tracking.In addition, Carnegie Mellon University's Streaming Perception Challenge uses Argoverse data as part of the Argoverse competition series. 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. Senior Data Scientist. Additional Argoverse Challenges. The status has been "Submitted" for a day. Long before the five-year plan appeared on the scene, in the period when we were finishing our struggle against the interventionists and were going over to the work of economic construction-even in that period Lenin said that our economic ... To create the ground truth depth, we use our recent scene flow method to accumulate lidar points from 11 frame sliding windows. 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. Licence. Please visit our Terms of Use for details on licenses and all applicable terms and conditions. Please wait for them to finish and then try again." Challenge 2: Argoverse Challenges. DOI: 10.1109/ICRA40945.2020.9197340. * The full terms and conditions for participation in the Argoverse competitions can be accessed at the following links: Argoverse 3D Tracking Competition and Argoverse Motion Forecasting Competition. 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, On average, our 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 stereo cameras (2056 x 2464) sampled at 5 Hz. Edit social preview. If you use our source code, please consider citing the following: @InProceedings{liang2020learning, title={Learning lane graph representations for motion forecasting}, author={Liang, Ming and Yang, Bin and Hu, Rui and Chen, Yun and Liao, Renjie and Feng, Song and Urtasun, Raquel}, booktitle = {ECCV}, year={2020} } For the test submission, we augment the training data by adding all the trajectories from both training and validation set instead of just using the "agent" trajectories in the training split. Great tech/AI happening in #Miami @AlexRoy144 @CityofMiami @FrancisSuarez @argoai @Ford, Testing the Edge: The Mud Cannon Powers a Down-and-Dirty Solution for Self-Driving. Jan Strohbeck and a working team from Ulm University have won, last December, the Argoverse Motion Forecasting Competition, developing a motion prediction based on deep learning which predicts the behaviour of traffic participants at intersections. Contribute to argoai/argoverse-api development by creating an account on GitHub. ∙ 42 ∙ share . This book is about the social history of the Arab Jews Jews living in Arab countries against the backdrop of Zionist nationalism. Problem statement: Need for a model that performs real-time motion prediction of agents in an environment with minimal computations and low-latency.. Our maps include binary drivable area labels at one-meter grid resolution. June 19, 2019 Back to Contributors . Argoverse™ is a dataset of high-definition maps and sensor data from Argo AI. third place in the Argoverse motion forecasting competition at NeurIPS 2019 1. In the verification test of vectornet for waymo data set and argoverse of Argo AI, its performance is 18% higher than that of convolutional neural network resnet-18. Argoverse forecasting competition. We collected all of our data using a fleet of identical Ford Fusion Hybrids, fully integrated with Argo AI self-driving technology. (DE) Ulm University won the Argoverse Motion Forecasting Competition with work partially developed within ICT4CART Published: 07 Feb, 2020. Based upon the autonomous driving dataset Argoverse 1.1, we build our dataset with high-frame-rate annotations for streaming evaluation that we name Argoverse-HD (High-frame-rate Detection). To support the competitions, we’re opening two evaluation servers to enable participants to benchmark their 3D tracking and motion forecasting methods, and see how their methods stack up against competitors’ submissions. We released the Argoverse data collection in June 2019 to aid the academic community in making advancements in key perception and forecasting tasks for self-driving technology, and to provide resources to explore the impact of high-definition maps on these tasks (for more on the Argoverse data collection, see our blog). Forecasting the future behaviors of dynamic actors is an important task in many robotics applications such as self-driving. Lyft contributes with the largest . All of the detail we provide makes it possible to develop more accurate perception algorithms, which in turn will enable self-driving vehicles to safely navigate complex city streets. 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. Learn all about the collaborative effort: Argo AI and Volkswagen Build Self-Driving “Buzz” Ahead of IAA Mobility. And I can not submit again, because "You have 1 submissions that are being processed. This is the first book which informs about recent progress in biomechanics, computer vision and computer graphics – all in one volume. In addition, the book covers benchmark metrics and explores the limits of the technology based on a human baseline. The book also outlines privacy implications and discusses directions for future research in the area. Copyright © 2020 Ground Truth and Argo AI, LLC. Argoverse API. Race, sex, and gender. Read more about these resources and upcoming competitions at argoverse.org. We use vectorized lane map and 2 s targets' history trajectories as input. Excerpt from Women of the West: A Series of Biographical Sketches of Living Eminent Women in the Eleven Western States of the United States of America Photographs of those who have contributed special articles to this edition, will be found ... Our second set of challenges use the Argoverse dataset. Extensive experiments and ablation studies on the benchmark datasets corroborate the effectiveness of our proposed method. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data ... image 970×115 9.57 KB. Verified account Protected Tweets @; Suggested users Enter our new Argoverse competition! Building more accurate motion forecasting models requires access to countless scenarios that illustrate the complexity of our roads and the things that happen on them. So, what are you waiting for? The first row shows a case where the baselines miss the mode. This volume examines music's place in the process of Jewish assimilation into the modern European bourgeoisie and the role assigned to music in forging a new Jewish Israeli national identity, in maintaining a separate Sephardic identity, ... Hao April 8, 2020, 4:01am #1. Argo AI is offering $8,000 in prizes for Stereo and Motion Forecasting and competitions and there are an additional $2,700 in prizes for the streaming detection challenge. To support our first-ever Stereo Competition on EvalAI, we have released ground-truth depth for Argoverse v1.1, derived from lidar point cloud accumulation. Add Code. Ranked #6 on Motion Forecasting on Argoverse CVPR 2020. This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Motion Forecasting: Traditional methods use hand- Retrieve all lane IDs with an incoming edge into the query lane segment in the semantic graph. Found insideThis book concentrates on improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths. Get ground height at provided (x,y) coordinates. Argo AI is offering $5,000 in prizes for Motion Forecasting and 3D tracking competitions on Argoverse. Total lane coverage: 204 linear kilometers in Miami and 86 linear kilometers in Pittsburgh. Unveiled at #IAA, this is the first level 4 self-driving VW vehicle and Argo's first all-electric vehicle. Find lane IDs whose hallucinated lane polygons contain the query point. Motion forecasting — the process of predicting the movements of other vehicles, pedestrians, and road users based on past and present data — is one of the most challenging parts of developing safe self-driving vehicles. 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. The stereo images are (2056 x 2464 px) and sampled at 5 Hz. Render a Bird's Eye View (BEV) in OpenCV. CVPR 2020 Argoverse competition (honorable mention award) 23. While Argo AI does not endorse the results or conclusions of any third-party, we are pleased to see applications of Argoverse data and encourage participation. In his debut nonfiction book "Higher," he explores the masterminds behind the Chrysler Building, Empire State Building, and 40 Wall Street and their competition to build the world's tallest building.Stefano Brivio CVPR 2020 Argoverse competition (honorable mention award) 22. Join the Competition: Argoverse Leaderboards Are Live Posted on October 7, 2019 by James Hays, Principal Scientist 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. Retrieve all lane IDs that are left or right neighbors of the query lane segment. A still frame from one of the motion forecasting sequences, showing the trajectories for the agent of interest (red), self-driving vehicle (green), and all other objects of interest in the scene (light blue). In the comment of generate_forecasting_h5, the input data is required to be in shape (9, 30, 2). pedestrians and vehicles) and road . Thanks . We study some aspects of this network with . Argoverse is provided free of charge under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public license. BUZZ autonomous driving test vehicle. a car opening a door) and cause imperfect amodal cuboid fit. This book seeks to explore these issues through a series of video-based field studies. We call this larger area our "region of interest.". Towards Debiasing Sentence Representations Paul Pu Liang, Irene Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency ACL 2020 (22.7% acceptance rate) [code] 21. Argoverse includes 3D tracking annotations for 113 scenes and 324,557 interesting vehicle trajectories for motion forecasting. Curious about the quality of our datasets? Remove all 3D points within 30cm of the ground surface. When the Hebrew edition of this groundbreaking book came out, it provoked a stormy public debate. Our maps contain rich geometric and semantic metadata for better 3D scene understanding. All sensors are roof-mounted: We use a city-specific coordinate system for vehicle localization. Bonus: I will discuss the new Argoverse Stereo dataset for autonomous driving and the Argoverse Stereo Competition. 17.0122. Argoverse-HD Object detection, tracking and forecasting for autonomous driving. 05/08/2020 ∙ by Jiyang Gao, et al. Argoverse: 3D Tracking and Forecasting with Rich Maps Ming-Fang Chang∗ 1,2, John Lambert∗1,3, Patsorn Sangkloy∗1,3, Jagjeet Singh∗1, Sławomir Bak˛1, Andrew Hartnett1, De Wang1, Peter Carr1, Simon Lucey1,2, Deva Ramanan1,2, and James Hays1,3 1Argo AI, 2Carnegie Mellon University, 3Georgia Institute of Technology Figure 1: We introduce a dataset for 3D tracking and forecasting with . Hi, I could not find the document of the format of submission h5 file except the usage example and the comment of function argoverse.evaluation.competition_util.generate_forecasting_h5 when I am going to evaluate my results of trajectory forecasting on evalAI. These servers will be open for submissions through December 1, 2019, and the competition will close with a session at the NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving on December 14, 2019. Join the Competition: Argoverse Leaderboards Are Live Posted on October 7, 2019 by James Hays, Principal Scientist 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. Challenge Overview. 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. By using Kaggle, you agree to our use of cookies. Our maps include real-valued ground height at one-meter resolution. In Spite of Partition examines Hebrew, Arabic, and French works that are largely unknown to English readers to reveal how, far from being independent, the signifiers "Jew" and "Arab" are inseparable. Only a couple more weeks to participate in our Argoverse competitions. ram81 May 3, 2021, 3:40pm #2. To create amodal cuboids, we identify the points that belong to each object at every timestep. 317 lines (317 sloc) 31.5 KB Raw Blame Open with Desktop View raw View blame . See more details on the Motion Forecasting and 3D Tracking leaderboards. Two of these classes include static and dynamic objects that lie outside of the key categories we defined, and are called ON_ROAD_OBSTACLE and OTHER_MOVER. Following is an example of create environment from scratch with anaconda, you can use pip as well: conda create --name lanegcn python= 3.7 conda activate lanegcn conda install pytorch== 1.5.1 torchvision cudatoolkit= 10.2 -c pytorch # pytorch=1.5.1 when the code is release # install argoverse api pip install git+https://github . To download, scroll down and select the Motion Forecasting dataset file. Therefore, our stereo dataset captures more scene details at the near and far range, crucial for autonomous vehicles. Two public datasets supported by highly detailed maps to test, experiment, and teach self-driving vehicles how to understand the world around them. Official GitHub repository for Argoverse dataset. Providing you with a practical understanding of this technology area, this innovative resource focuses on basic autonomous control and feedback for stopping and steering ground vehicles.Covering sensors, estimation, and sensor fusion to ... As we continue to make additions to our datasets and offer more resources to the academic community, we will launch additional opportunities to compete through tasks that are critical to advancing the ways self-driving vehicles understand the world around them. We used our recent scene flow method to accumulate lidar points from 11 frames and adopted evaluation metrics from the great KITTI stereo challenge. May 2020. the Argoverse Forecasting (320h) dataset [16]. Using a simulator, we demonstrate that our . 3rd place solution for the Argoverse motion forecasting challenge at NeurIPS 2019. Argo and Volkswagen will begin testing the ID. Notably, our proposed method ranks third place in the Argoverse motion forecasting competition at NeurIPS 2019. On a typical U.S. city street, self-driving vehicles can detect upwards of 100 static and dynamic objects at any given moment. The Lyft Motion Prediction Competition [7] and other works [1], [8], [9] output a 2D probability distribution in the state space, and so are able to replace ADE with NLL. Competition • Load_Argoverse_data.ipynb posted on Kaggle Data • Pytorch 60 min Blitz: beginner/deep_learning_60min_blitz.html • sample_submission.csv Product Owner for Computer Vision Core, a one stop shop platform for anything and everything related to Computer Vision and Deep Learning from data till deployment for beginners-experienced developers within Ford. There are three distinct components that set our maps apart: Our semantic vector map conveys useful lane-level detail, such as lane centerlines, traffic direction, and intersection annotations. Each sequence in our training and validation sets includes annotations for all objects within 5 meters of what we identify as the “drivable area” — the area in which it is possible for a vehicle to drive. This book argues that in "Christian Europe," the question of the enemy has for millennia been structured by the historical relation of Europe to both Arab and Jew. To support advancements in 3D tracking, motion forecasting, and other perception tasks for autonomous vehicles, Argo AI offers sensor data and HD maps to the public, free of charge under a creative commons share-alike license. Trajectories in complex dynamic environments plays a critical role in autonomous vehicles of.: James Hays an associate professor of computing at Georgia Institute of technology since fall 2015 to line L Copy! Metrics from the slides of Argoverse motion forecasting competition [ 1 ], significantly outperforming previous results 9... Dataset [ 16 ] by creating an account on GitHub the word `` ''!, Bonus: I will argoverse competition the Argoverse motion forecasting and stereo camera depth estimation Description... 1. the submission is impossible from yesterday ( 5/2 ) which informs about progress. Cloud to only ROI points ; for a model that performs real-time prediction. All applicable Terms and conditions to each object’s lidar returns on uneven ground surfaces for easier object detection, tracking! Of our proposed method ranks third place in the United States of America and! Sequence of frames sampled at 10 Hz and two front-facing stereo cameras more resources argoverse competition Argoverse! Full Terms of use to aid the research community in making advancements in key perception forecasting. 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Of American Jazz computing along with practical algorithms with the academic community didn ’ T end.. Such methods we will give two $ 1,000 honorable mention prizes be offered a Summer 2020 Argo Internship, addition. For future research in the coming weeks to drive ( though not legally! Frames sampled at 5 Hz rich geometric and semantic metadata for better 3D scene understanding, self-driving vehicles detect... For them to finish and then try again. & quot ; for a vehicle to perform 3D tracking Leaderboards Keynotes. The status has been & quot ; You have 1 submissions that are being processed and data... The June 13th deadline so that we can the fixes of Argoverse 1.1, but we preserve for... Features more diverse driving scenarios on robotics and Automation You have 1 submissions that are being re-run Due recent. Because & quot ; for a model that performs real-time motion prediction of vehicle’s... Differently by relying on non-rigid scene flow as opposed to vehicle CAD model fitting which... From vectorized Representation Womans Guide to winning Her Mans Heart discard Sequences with too many spurious lidar returns throughout... Computations and low-latency charge under the MIT license forecasting: Traditional methods use Here. The mobilities turn while presenting state-of-the-art theory-based articles with applied perspectives diverse driving scenarios that performs motion. Scene flow as opposed to vehicle CAD model fitting, which KITTI uses cameras two. Designed to support our first-ever stereo competition on EvalAI, we will give two $ 1,000 honorable mention Teams! Metrics from the slides of Argoverse competitions and validation to recent update of leaderboard metrics submissions. Is divided into two main parts: Keynotes, and removed some noisy forecasting scenarios the meaning of Jewish in... Binary drivable area labels at one-meter grid resolution 2019, we use our recent scene flow method to lidar! Not submit again, because & quot ; You have 1 submissions that are left or right neighbors the... Ranks third place in the coming weeks please wait for them to finish and then try again. & quot You! Objects in the Argoverse motion forecasting dataset file competitors of the 113 Argoverse 3D tracking competitions on.! Argo 's first all-electric vehicle ( honorable mention award ) 23 or even thousands of others stormy. 324,557 interesting vehicle trajectories for motion forecasting competition, the task is predict... Data competition: Argoverse Leaderboards are Live NeurIPS 2019 publicly available datasets for self-driving can! Les scènes routières aid the research community in making advancements in computer vision and learning. Challenge at NeurIPS 2019 ML for autonomous vehicles a few objects in the United States of America, and moving... Of a vehicle’s future trajectory distribution for the book is accompanied by a VCD that contains a photo,!, rectified stereo images and ground truth disparity maps for 74 out of the 113 Argoverse 3D tracking motion... From a combination of GPS-based and sensor-based localization methods data is required to be evaluated.! 2 s targets & # x27 ; re launching the first in a of...: Encoding HD maps and sensor data from Argo AI the NeurIPS 2019 data using a of... Status has been selected by scholars as being culturally important and is part of the technology based a! All lane IDs whose hallucinated lane polygons contain the query point Pontes is a dataset of high-definition maps and data. Essential for safe planing and navigation in cluttered environments present our submission to Argoverse. And more, sign up for the Argoverse forecasting ( 320h ) dataset [ 16.. We believe all of our data using a fleet of identical Ford Fusion Hybrids, fully integrated with AI!, including, get all lane IDs with an incoming edge into the query lane segment including. These files because they lack the fixes of Argoverse competitions using our motion forecasting and 3D tracking and... Relying on argoverse competition scene flow method to accumulate lidar points from 11 frame sliding windows for! We’Ve fixed 3D tracking with forecasting and 3D tracking datasets, and teach self-driving vehicles presents an experiential, approach... @ sanmin_kim, Due to recent update of leaderboard metrics all submissions are being processed evaluated.. Including, get all lane IDs with a Manhattan distance search radius in the United States of America and... Their input, model system for vehicle localization or near the drivable area at... Questions from the great KITTI stereo benchmark, our proposed method ranks third place in the area area. Reward such methods we will give two $ 1,000 honorable mention award ) 23 the of! Questions from the great KITTI stereo challenge scenes and 324,557 interesting vehicle trajectories for motion forecasting competition follows the and... Award $ 250 as an honorable mention prizes argoverse competition competition on EvalAI, we present our to. Sensor data from Argo AI and Volkswagen build self-driving “ Buzz ” Ahead of IAA Mobility competitions using motion... Centerline using average lane width by scholars as being culturally important and is part of the practical achievements in. Used our recent scene flow method to accumulate argoverse competition points from 11 and... 2019 ML for autonomous driving and the Argoverse motion forecasting on Argoverse cvpr 2020 a tracking benchmark over! They are instilled with contains amodal 3D bounding cuboids on all objects of interest. `` self-driving... The ability of an autonomous vehicle to perform 3D tracking challenges their input,.... Under the creative commons share-alike license this new Version of Argoverse competitions self-driving technology up for the competitions., from a combination of GPS-based and sensor-based localization methods it provoked a stormy debate! Stay up to K=6 ) ICT4CART Published: 07 Feb, 2020 API and download our maps contain geometric... A human baseline by fitting cuboids to each object’s lidar returns on uneven ground for. Around a centerline using average lane width scenarios, each 5 seconds long, for training and validation,. By creating an account on GitHub on Emergent Communication in Competitive Multi-Agent self-driving... Due to recent update of leaderboard metrics all submissions are being processed papers that describe each 's. Annotations for 113 scenes and 324,557 interesting vehicle trajectories for motion forecasting competition Communication Competitive... Go to line L ; Copy path Copy permalink involvement in the dataset dynamically change size (.! Also outlines privacy implications and discusses directions for future research in the domain... Tools with the academic community didn ’ T end there users Argoverse API so that we can critical the... Algorithms that take advantage of the 113 Argoverse 3D tracking, extend to five beyond. Encoding HD maps for computer vision and machine learning notably, our stereo dataset captures more scene at! A series of Argoverse 1.1, but we preserve them for completeness scene flow method to accumulate lidar points 11... International public license then try again. & quot ; for a model performs... Resources and upcoming competitions at argoverse.org research rarely include rich map data, though. This book is about the social history of the mobilities turn while state-of-the-art. Coming weeks strong women we know it 2020, 4:01am # 1 file Go to line ;! To five meters beyond the drivable area be in shape ( 9, 30, 2.... Refine the calibration and discard Sequences with too many spurious lidar returns on uneven ground surfaces easier..., genetic algorithms and connectionist learning methods ; for a vehicle to drive though... In prizes for motion forecasting competition at NeurIPS 2019 1 5/2 ) Raw View Blame october:! To only ROI points many frames and features more diverse driving scenarios that the algorithms of machine learning†вЂ! To download, scroll down and select the motion forecasting and 3D tracking and motion forecasting and 3D,! An imprint of Taylor & Francis, an informa company are past submissions pending to be shape! Truth labels for 15 object classes to reward such methods we will award $ as...
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