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Traffic safety science has long been hindered by a fundamental data paradox: the crashes we most wish to prevent are precisely those events we rarely observe.
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Survey of multi-objective optimization methods for engineering
R Timothy Marler and Jasbir S Arora · 2004
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan Koenig and Andrew Howard · 2004
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Poisson, poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory
Dominique Lord, Simon P Washington, and John N Ivan · 2005
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Robust reinforcement learning
Jun Morimoto and Kenji Doya · 2005
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The 100-car naturalistic driving study, phase ii-results of the 100-car field experiment
Thomas A Dingus, Sheila G Klauer, Vicki Lewis Neale, Andy Petersen, Suzanne E Lee, Jeremy Sudweeks, Miguel A Perez, Jonathan Hankey, David Ramsey, Santosh Gupta, et al · 2006
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Validating a driving simulator using surrogate safety measures
Xuedong Yan, Mohamed Abdel-Aty, Essam Radwan, Xuesong Wang, and Praveen Chilakapati · 2008
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The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives
Dominique Lord and Fred Mannering · 2010
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Design of the in-vehicle driving behavior and crash risk study: in support of the SHRP 2 naturalistic driving study
Jon Antin · 2011
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Multiple-vehicle collision induced by a sudden stop in traffic flow
Naoki Sugiyama and Takashi Nagatani · 2012
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The australian 400-car naturalistic driving study: Innovation in road safety research and policy
Michael Arthur Regan, Aa Williamson, Raphael Grzebieta, J Charlton, M Lenne, B Watson, Nc Haworth, Andry Rakotonirainy, Jd Woolley, Rd Anderson, et al · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Freeway safety estimation using extreme value theory approaches: A comparative study
Lai Zheng, Karim Ismail, and Xianghai Meng · 2014
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Drivers’ rear end collision avoidance behaviors under different levels of situational urgency
Xuesong Wang, Meixin Zhu, Ming Chen, and Paul Tremont · 2016
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Understanding traffic crash under-reporting: linking police and medical records to individual and crash characteristics
Kira H Janstrup, Sigal Kaplan, Tove Hels, Jens Lauritsen, and Carlo G Prato · 2016
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Learning deep representation from big and heterogeneous data for traffic accident inference
Quanjun Chen, Xuan Song, Harutoshi Yamada, and Ryosuke Shibasaki · 2016
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Causal inference and the data-fusion problem
Elias Bareinboim and Judea Pearl · 2016
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The study design of udrive: the naturalistic driving study across europe for cars, trucks and scooters
Yvonne Barnard, Fabian Utesch, Nicole van Nes, Rob Eenink, and Martin Baumann · 2016
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Risk-aware planning: Methods and case study on safe driving route
John Krumm and Eric Horvitz · 2017
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Combining satellite imagery and open data to map road safety
Alameen Najjar, Shun’ichi Kaneko, and Yoshikazu Miyanaga · 2017
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data
Zhuoning Yuan, Xun Zhou, and Tianbao Yang · 2018
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Anticipating traffic accidents with adaptive loss and large-scale incident db
Tomoyuki Suzuki, Hirokatsu Kataoka, Yoshimitsu Aoki, and Yutaka Satoh · 2018
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Safety prediction with datasets characterised with excess zero responses and long tails
Dominique Lord and Srinivas Reddy Geedipally · 2018
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Safe mobility: Challenges, methodology and solutions
Dominique Lord and Simon Washington · 2018
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An improved deep learning model for traffic crash prediction
Chunjiao Dong, Chunfu Shao, Juan Li, and Zhihua Xiong · 2018
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Estimating the expected number of crashes with traffic conflicts and the lomax distribution–a theoretical and numerical exploration
Andrew P Tarko · 2018
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Adaptive stress testing for autonomous vehicles
Mark Koren, Saud Alsaif, Ritchie Lee, and Mykel J Kochenderfer · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Rachel Faulkner, et al · 2018
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Scalable end-to-end autonomous vehicle testing via rare-event simulation
Matthew O’Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, and John C Duchi · 2018
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Deepproblog: Neural probabilistic logic programming
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt · 2018
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Modeling car-following behavior on urban expressways in shanghai: A naturalistic driving study
Meixin Zhu, Xuesong Wang, Andrew Tarko, and Shou’en Fang · 2018
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Viena2: A driving anticipation dataset
Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann, Basura Fernando, Lars Petersson, and Lars Andersson · 2018
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Chrono: An open-source multi-physics simulation package
Radu Serban, Alessandro Tasora, Dan Negrut, et al · 2018
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Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks
Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, and Silvio Savarese · 2019
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Stochastic driver modeling and validation with traffic data
Mert Albaba, Yildiray Yildiz, Nan Li, Ilya Kolmanovsky, and Anouck Girard · 2019
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Aads: Augmented autonomous driving simulation using data-driven algorithms
Wei Li, CW Pan, Rong Zhang, JP Ren, YX Ma, Jin Fang, FL Yan, QC Geng, XY Huang, HJ Gong, et al · 2019
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Integrating macro-and micro-level safety analyses: a bayesian approach incorporating spatial interaction
Qing Cai, Mohamed Abdel-Aty, Jaeyoung Lee, and Helai Huang · 2019
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Vehicle trajectory prediction at intersections using interaction based generative adversarial networks
Debaditya Roy, Tetsuhiro Ishizaka, C Krishna Mohan, and Atsushi Fukuda · 2019
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Causal discovery with reinforcement learning
Shengyu Zhu, Ignavier Ng, and Zhitang Chen · 2019
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Artur d’Avila Garcez, Marco Gori, Luis C Lamb, Luciano Serafini, Michael Spranger, and Son N Tran · 2019
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The potential of naturalistic driving for in-depth understanding of driver behavior: Udrive results and beyond
Nicole van Nes, Jonas Bärgman, Michiel Christoph, and Ingrid van Schagen · 2019
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Lyft level 5 av dataset 2019
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet · 2019
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Crash to not crash: Learn to identify dangerous vehicles using a simulator
Hoon Kim, Kangwook Lee, Gyeongjo Hwang, and Changho Suh · 2019
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Riskoracle: A minute-level citywide traffic accident forecasting framework
Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, and Hengchang Liu · 2020
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Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis
Fred Mannering, Chandra R Bhat, Venky Shankar, and Mohamed Abdel-Aty · 2020
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Social-stgcnn: Social spatio-temporal graph convolutional neural networks for human trajectory prediction
Shreya Gong, Mark Hoogendoorn, Yike Lu, and Matthew Turk · 2020
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Multi-modal agent trajectory prediction with local self-attention contexts
Manoj Bhat and Jonathan Francis · 2020
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Multimodal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network
Rowan McAllister · 2020
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Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data
Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone · 2020
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Learning robust control policies for end-to-end autonomous driving from data-driven simulation
Alexander Amini, Igor Gilitschenski, Jacob Phillips, Julia Moseyko, Rohan Banerjee, Sertac Karaman, and Daniela Rus · 2020
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Phantom of the adas: Securing advanced driver-assistance systems from split-second phantom attacks
Ben Nassi, Yisroel Mirsky, Dudi Nassi, Raz Ben-Netanel, Oleg Drokin, and Yuval Elovici · 2020
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Pip: Planning-informed trajectory prediction for autonomous driving
Haoran Song, Wenchao Ding, Yuxuan Chen, Shaojie Shen, Michael Yu Wang, and Qifeng Chen · 2020
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Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Lgsvl simulator: A high fidelity simulator for autonomous driving
Guodong Rong, Byung Hyun Shin, Hadi Tabatabaee, Qiang Lu, Steve Lemke, Mārtiņš Možeiko, Eric Boise, Geehoon Uhm, Mark Gerow, Shalin Mehta, et al · 2020
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Synchrono: A scalable, physics-based simulation platform for testing groups of autonomous vehicles and/or robots
Jay Taves, Asher Elmquist, Aaron Young, Radu Serban, and Dan Negrut · 2020
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Muhammad Monjurul Karim, Yu Li, Ruwen Qin, and Zhaozheng Yin · 2021
Hypergraph-based motion generation with multi-modal interaction relational reasoning
Keshu Wu, Yang Zhou, Haotian Shi, Dominique Lord, Bin Ran, and Xinyue Ye · 2024
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Pedsumo: Simulacra of automated vehicle-pedestrian interaction using sumo to study large-scale effects
Mark Colley, Julian Czymmeck, Mustafa Kücükkocak, Pascal Jansen, and Enrico Rukzio · 2024
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Enhancing vehicular platoon stability in the presence of communication cyberattacks: A reliable longitudinal cooperative control strategy
Zihao Li, Yang Zhou, Yunlong Zhang, and Xiaopeng Li · 2024
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Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions
Haicheng Liao, Haoyu Sun, Huanming Shen, Chengyue Wang, Chunlin Tian, KaHou Tam, Li Li, Chengzhong Xu, and Zhenning Li · 2024
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Highway safety analytics and modeling
Dominique Lord, Xiao Qin, and Srinivas R Geedipally · 2021
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Compositional training for end-to-end deep auc maximization
Zhuoning Yuan, Zhishuai Guo, Nitesh Chawla, and Tianbao Yang · 2021
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Deep auc maximization for medical image classification: Challenges and opportunities
Tianbao Yang · 2021
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Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification
Zhuoning Yuan, Yan Yan, Milan Sonka, and Tianbao Yang · 2021
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A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling
Chen Wang, Yuanchang Xie, Helai Huang, and Pan Liu · 2021
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Trafficsim: Learning to simulate realistic multi-agent behaviors
Simon Suo, Sebastian Regalado, Sergio Casas, and Raquel Urtasun · 2021
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Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment
Shuo Feng, Xintao Yan, Haowei Sun, Yiheng Feng, and Henry X Liu · 2021
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Meng Wang, Zach Noonan, Pnina Gershon, and Shannon C Roberts · 2024
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Beyond 1d and oversimplified kinematics: A generic analytical framework for surrogate safety measures
Sixu Li, Mohammad Anis, Dominique Lord, Hao Zhang, Yang Zhou, and Xinyue Ye · 2024
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Disturbances and safety analysis of linear adaptive cruise control for cut-in scenarios: A theoretical framework
Zihao Li, Yang Zhou, Danjue Chen, and Yunlong Zhang · 2024
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Safeshift: Safety-informed distribution shifts for robust trajectory prediction in autonomous driving
Benjamin Stoler, Ingrid Navarro, Meghdeep Jana, Soonmin Hwang, Jonathan Francis, and Jean Oh · 2024
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Safety-critical scenario generation via reinforcement learning based editing
Haolan Liu, Liangjun Zhang, Siva Kumar Sastry Hari, and Jishen Zhao · 2024
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Scenediffuser: Efficient and controllable driving simulation initialization and rollout
Chiyu Max Jiang, Yijing Bai, Andre Cornman, Christopher Davis, Xiukun Huang, Hong Jeon, Sakshum Kulshrestha, John Lambert, Shuangyu Li, Xuanyu Zhou, Carlos Fuertes, Chang Yuan, Mingxing Tan, Yin Zhou, and Dragomir Anguelov · 2024
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Chatscene: Knowledge-enabled safety-critical scenario generation for autonomous vehicles
Jiawei Zhang, Chejian Xu, and Bo Li · 2024
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Collision risk assessment for intelligent vehicles considering multi-dimensional uncertainties
Zhenhai Gao, Mingxi Bao, Taisong Cui, Fangyuan Shi, Xianqing Chen, Wenhao Wen, Fei Gao, and Rui Zhao · 2024
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Framework to generate hypergraphs with community structure
Nicolò Ruggeri, Federico Battiston, and Caterina De Bacco · 2024
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Heterogeneous hypergraph embedding for node classification in dynamic networks
Malik Khizar Hayat, Shan Xue, Jia Wu, and Jian Yang · 2024
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Advdiffuser: Generating adversarial safety-critical driving scenarios via guided diffusion
Yuting Xie, Xianda Guo, Cong Wang, Kunhua Liu, and Long Chen · 2024
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Crash: Challenging reinforcement-learning based adversarial scenarios for safety hardening
Amar Kulkarni, Shangtong Zhang, and Madhur Behl · 2024
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Neural radiance field in autonomous driving: A survey
Lei He, Leheng Li, Wenchao Sun, Zeyu Han, Yichen Liu, Sifa Zheng, Jianqiang Wang, and Keqiang Li · 2024
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2d gaussian splatting for geometrically accurate radiance fields
Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao · 2024
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Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering
Antoine Guédon and Vincent Lepetit · 2024
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Freesim: Toward free-viewpoint camera simulation in driving scenes
Lue Fan, Hao Zhang, Qitai Wang, Hongsheng Li, and Zhaoxiang Zhang · 2024
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Counterfactual causal inference in natural language with large language models
Gaël Gendron, Jože M Rožanec, Michael Witbrock, and Gillian Dobbie · 2024
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Chengzhengxu Li, Xiaoming Liu, Yichen Wang, Duyi Li, Yu Lan, and Chao Shen · 2024
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Prompting multi-modal tokens to enhance end-to-end autonomous driving imitation learning with llms
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Learning autonomous driving tasks via human feedbacks with large language models
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