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Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs [article]

Rohan Chandra, Tianrui Guan, Srujan Panuganti, Trisha Mittal, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
2020 arXiv   pre-print
We present a novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning.  ...  We use a two-stream graph-LSTM network to perform traffic forecasting using these weighted DGGs.  ...  ACKNOWLEDGEMENTS This work was supported in part by ARO Grants W911NF1910069 and W911NF1910315, Semiconductor Research Corporation (SRC), and Intel.  ... 
arXiv:1912.01118v2 fatcat:vpeofkybtbcmdhjd7sea3cssda

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Bogdan Ilie Sighencea, Rareș Ion Stanciu, Cătălin Daniel Căleanu
2021 Sensors  
an overview of the available datasets, performance metrics used in the evaluation process, and practical applications.  ...  Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21227543 pmid:34833619 pmcid:PMC8619260 fatcat:u7wapci74jdlpcbpn2uu5ljomy

A Review on Scene Prediction for Automated Driving

Anne Stockem Stockem Novo, Martin Krüger, Marco Stolpe, Torsten Bertram
2022 Physics  
Moreover, it also shows the problem of inter-model comparison, as many publications do not use standardized test sets.  ...  This way, a safe and smooth driving experience can be guaranteed. The complex spatio-temporal dependencies and high dynamics are some of the biggest challenges for scene prediction.  ...  The new approach takes a hypothetical ego trajectory and integrates this information in the multi-agent forecasting.  ... 
doi:10.3390/physics4010011 fatcat:4rbh64feprdnrflcf4ydyyre4a

BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction [article]

Beihao xia, Conghao Wong, Heng Li, Shiming Chen, Qinmu Peng, Xinge You
2020 arXiv   pre-print
of activities in different periods.  ...  Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors.  ...  Introduction Trajectory prediction is to forecast agents' locations in the future based on their past positions.  ... 
arXiv:2010.03897v1 fatcat:fruw4esvcbcnnmsyuhv63vu5vi

Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis [article]

Farid Talebloo, Emad A. Mohammed, Behrouz H. Far
2021 arXiv   pre-print
Improper driving results in fatalities, damages, increased energy consumptions, and depreciation of the vehicles. Analyzing driving behaviour could lead to optimize and avoid mentioned issues.  ...  By identifying the type of driving and mapping them to the consequences of that type of driving, we can get a model to prevent them.  ...  They developed the idea of graph-graph (definition = trajectories with similar driving behaviour in the graph-graph peer dependency should have near representations in the learnt representation feature  ... 
arXiv:2109.08996v1 fatcat:r2faox3pdrfedb72ewkecpp64a

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
Then, using a novel developed method, it detects congested roads and using a multimetric fitness function, applies re-routing to vehicles to ease the traffic congestion in the area, and avoids the traffic  ...  Nickray This article proposes a new dynamic multilayer and fogcloud-based advance route guidance system architecture in order to detect and ease the road congestion.  ...  Two definitions of a platoon are proposed and verified using agent-based simulations to find the one that provides the most realistic representation of traffic behavior in a stochastic model.  ... 
doi:10.1109/tits.2020.3044830 fatcat:mmamywaconchdnynve3qm6eip4

Quantum Machine Learning for Finance [article]

Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur Rattew, Yue Sun (+1 others)
2021 arXiv   pre-print
In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term.  ...  This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.  ...  A quantum spectral clustering algorithm for data represented as a graph has also been proposed [43] .  ... 
arXiv:2109.04298v1 fatcat:7mrhh6b6hza73l7cn4c3uygxga

4G LTE Network Data Collection and Analysis along Public Transportation Routes

Habiba Elsherbiny, Ahmad M. Nagib, Hatem Abou-zeid, Hazem M. Abbas, Hossam S. Hassanein, Aboelmagd Noureldin, Akram Bin Sediq, Gary Boudreau
2020 GLOBECOM 2020 - 2020 IEEE Global Communications Conference  
I also wish to thank Basia Palmer for her patient and accurate review of this thesis and her useful suggestions.  ...  For time series forecasting, we used statistical methods as well as deep learning architectures.  ...  We used two common approaches for time series forecasting, namely ARIMA and LSTM models.  ... 
doi:10.1109/globecom42002.2020.9348031 fatcat:h6pelhjy7fbzjh7wbcbitnyrua

Table of Contents

2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
Optimization of the Spatial Positioning of Agents in Virtual Environments Marcos S.  ...  of EV Detour-to-Recharge Behavior Modeling and Charging Station Deployment Tianshu Ouyang, Jiahong Cai, Yuxuan Gao, Xinyan He, Huimiao Chen and Kexin Hang.......... 1265 Parallel LSTM Architectures for  ... 
doi:10.1109/ssci47803.2020.9308155 fatcat:hyargfnk4vevpnooatlovxm4li

Graph Deep Learning: State of the Art and Challenges

S. Georgousis, M. P. Kenning, X. Xie
2021 IEEE Access  
The graph has emerged as a particularly useful geometrical object in deep learning, able to represent a variety of irregular domains well.  ...  We identify four major challenges in graph deep learning: dynamic and evolving graphs, learning with edge signals and information, graph estimation, and the generalization of graph models.  ...  [72] proposed MinCutPool, which solves the same objective as spectral clustering with the use of an MLP.  ... 
doi:10.1109/access.2021.3055280 fatcat:7ruskzkdkjgkfkia7drmww6lse

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey [article]

Julian Wörmann, Daniel Bogdoll, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Tobias Gleißner, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels (+34 others)
2022 arXiv   pre-print
As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge.  ...  However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training.  ...  Most of the existing behavior prediction approaches perform simultaneous tracking and forecasting with the use of Kalman Filters or in the form of rule based approaches, as can be seen from the previous  ... 
arXiv:2205.04712v1 fatcat:u2bgxr2ctnfdjcdbruzrtjwot4

IA Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Mduduzi C. Hlophe, Bodhaswar T. Maharaj
2021 IEEE Access  
Compared with the ARIMA, the LSTM scheme obtained superior results in radio frequency spectral prediction.  ...  However, in the case of the Monte-Carlo technique, the agent must sample multiple trajectories and then uses an empirically computed cumulative discounted reward, v t , as an unbiased estimate of Q π θ  ... 
doi:10.1109/access.2021.3104099 fatcat:ucyvpx36drdj5dcl2npxipkhd4

A two-stage heuristic for the university course timetabling problem [chapter]

Máté Pintér, Balázs Dávid
2019 StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference  
Published by University of Primorska Press Titov trg 4, si-6000 Koper Editor-in-Chief Jonatan Vinkler Managing Editor Alen Ježovnik Koper, 2019 isBN 978-961-7055-82-5 (pdf)  ...  LSTMs are found usable in broad areas, such as sentence classification [2] , trajectory prediction of autonomous vehicles [9] , flood forecasting [8] and malware detection [15] .  ...  Furthermore, LSTM are used in engineering for estimating remaining useful life of systems [17] and in medicine for automated diagnosis of arrhythmia [11] .  ... 
doi:10.26493/978-961-7055-82-5.27-30 fatcat:mv36atnxqvczjg7m7aetrpvy6y

A review of machine learning applications in wildfire science and management [article]

Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, Mike D Flannigan
2020 arXiv   pre-print
We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection  ...  There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science.  ...  The authors would also like to thank Intact Insurance and the Western Partnership for Wildland Fire Science for their support.  ... 
arXiv:2003.00646v1 fatcat:5ufhtbwlsvd2rdk3ogbmqpnxuu

Certifying Unstability of Switched Systems Using Sum of Squares Programming

Benoît Legat, Pablo Parrilo, Raphaël Jungers
2020 SIAM Journal of Control and Optimization  
Therefore, the differential reachability and observability Gramians can be computed trajectory-wise by using impulse and initial output responses of dΣ along each φ t−t 0 (x 0 , u).  ...  Derived from the aforementioned results, we explain the behavior of myxobacteria during the formation of fruiting bodies.  ...  In particular, LSTM and CNN architectures are tested for forecasting day-ahead prices during the interval 2010-2016.  ... 
doi:10.1137/18m1173460 fatcat:ytlzbwk7vbampbuyo6snenz33m
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