Open Access

Uncertainty Estimation for Neural Time Series with an Application to Sideslip Angle Estimation

Journal Article
12-04-03-0020
ISSN: 2574-0741, e-ISSN: 2574-075X
Published August 19, 2021 by SAE International in United States
Uncertainty Estimation for Neural Time Series with an Application to
                    Sideslip Angle Estimation
Sector:
Citation: Ayyad, A., Prohm, C., Gräber, T., Unterreiner, M. et al., "Uncertainty Estimation for Neural Time Series with an Application to Sideslip Angle Estimation," SAE Intl. J CAV 4(3):247-259, 2021, https://doi.org/10.4271/12-04-03-0020.
Language: English

References

  1. Gräber , T. , Lupberger , S. , Unterreiner , M. , and Schramm , D. A Hybrid Approach to Side-Slip Angle Estimation with Recurrent Neural Networks and Kinematic Vehicle Models IEEE Transactions on Intelligent Vehicles 4 1 2018 39 47
  2. Gal , Y. and Ghahramani , Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning International Conference on Machine Learning New York 1050 1059 2016
  3. Kegelman , J.C. , Harbott , L.K. , and Gerdes , J.C. Insights into Vehicle Trajectories at the Handling Limits: Analysing Open Data from Race Car Drivers Vehicle System Dynamics 55 2 2017 191 207 https://doi.org/10.1080/00423114.2016.1249893
  4. Hooper , J. 2004 https://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert/ 2020
  5. Waymo 2018 https://storage.googleapis.com/sdc-prod/v1/safety-report/Safety%20Report%202018.pdf 2020
  6. He , K. , Zhang , X. , Ren , S. , and Sun , J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification Proceedings of the IEEE International Conference on Computer Vision Santiago, Chile 1026 1034 2015
  7. Silver , D. , Schrittwieser , J. , Simonyan , K. , Antonoglou , I. et al. Mastering the Game of Go without Human Knowledge Nature 550 7676 2017 354 359
  8. CiresAn , D. , Meier , U. , Masci , J. , and Schmidhuber , J. Multicolumn Deep Neural Network for Traffic Sign Classification Neural Networks 32 2012 333 338
  9. Ardila , D. , Kiraly , A.P. , Bharadwaj , S. , Choi , B. et al. End-to-End Lung Cancer Screening with Three-Dimensional Deep Learning on Low-Dose Chest Computed Tomography Nature Medicine 25 6 2019 954
  10. Mnih , V. , Kavukcuoglu , K. , Silver , D. , Rusu , A.A. et al. Human-Level Control through Deep Reinforcement Learning Nature 518 7540 2015 529
  11. Li , H. , Yu , D. , and Braun , J.E. A Review of Virtual Sensing Technology and Application in Building Systems HVAC&R Research 17 5 2011 619 645
  12. Schramm , D. , Hiller , M. , and Bardini , R. Vehicle Dynamics: Modeling and Simulation Berlin, Heidelberg Springer 2014 151
  13. Goodfellow , I. , Bengio , Y. , and Courville , A. Deep Learning Cambridge MIT Press 2016
  14. Kendall , A. and Gal , Y. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Advances in Neural Information Processing Systems Long Beach, CA 5574 5584 2017
  15. Bishop , C.M. Novelty Detection and Neural Network Validation IEE Proceedings-Vision, Image and Signal Processing 141 4 1994 217 222
  16. Gustafsson , F. , Persson , N. , Drevö , M. , Forssell , U. et al. Virtual Sensors of Tire Pressure and Road Friction Linköping Linköping University Electronic Press 2001
  17. Shraim , H. , Ananou , B. , Fridman , L. , Noura , H. et al. Sliding Mode Observers for the Estimation of Vehicle Parameters, Forces and States of the Center of Gravity Proceedings of the 45th IEEE Conference on Decision and Control San Diego, CA 1635 1640 2006
  18. Doumiati , M. , Victorino , A. , Charara , A. , and Lechner , D. Virtual Sensors, Application to Vehicle Tire-Road Normal Forces for Road Safety 2009 American Control Conference St. Louis, MO 3337 3343 2009
  19. Röckl , M. , Strang , T. , and Kranz , M. 2008 https://doi.org/10.1109/VETECF.2008.463
  20. Canale , M. , Fagiano , L. , Ruiz , F. , and Signorile , M.C. A Study on the Use of Virtual Sensors in Vehicle Control 2008 47th IEEE Conference on Decision and Control Cancun, Mexico 4402 4407 2008
  21. Melzi , S. and Sabbioni , E. On the Vehicle Sideslip Angle Estimation through Neural Networks: Numerical and Experimental Results Mechanical Systems and Signal Processing 25 6 2011 2005 2019 https://doi.org/10.1016/j.ymssp.2010.10.015
  22. Sasaki , H. and Nishimaki , T. A Side-Slip Angle Estimation Using Neural Network for a Wheeled Vehicle SAE Technical Paper 2000-01-0695 2000 https://doi.org/10.4271/2000-01-0695
  23. Der Kiureghian , A. and Ditlevsen , O. Aleatory or Epistemic? Does it Matter? Structural Safety 31 2 2009 105 112
  24. Pedregosa , F. , Varoquaux , G. , Gramfort , A. , Michel , V. et al. Scikit-Learn: Machine Learning in Python Journal of Machine Learning Research 12 2011 2825 2830
  25. Zhu , L. and Laptev , N. Deep and Confident Prediction for Time Series at Uber 2017 IEEE International Conference on Data Mining Workshops (ICDMW) New Orleans, LA 103 110 2017
  26. Lakshminarayanan , B. , Pritzel , A. , and Blundell , C. Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles Advances in Neural Information Processing Systems Long Beach, CA 6402 6413 2017
  27. Chindamo , D. and Gadola , M. Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network MATEC Web of Conferences 166 02001 2018
  28. Guo , Y. , Liao , W. , Wang , Q. , Yu , L. et al. Multidimensional Time Series Anomaly Detection: A GRU-Based Gaussian Mixture Variational Autoencoder Approach Asian Conference on Machine Learning Beijing, China 97 112 2018
  29. Run-Qing , C. , Shi , G.-H. , Zhao , W.-L. , and Liang , C.-H. 2014
  30. Marchi , E. , Vesperini , F. , Eyben , F. , Squartini , S. et al. A Novel Approach for Automatic Acoustic Novelty Detection Using a Denoising Autoencoder with Bidirectional LSTM Neural Networks 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Queensland, Australia 1996 2000 2015
  31. Yoo , Y.-H. , Kim , U.-H. , and Kim , J.-H. 2019
  32. Malhotra , P. , Ramakrishnan , A. , Anand , G. , Vig , L. et al. 2016
  33. Taylor , A. , Leblanc , S. , and Japkowicz , N. Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Montreal, Canada 130 139 2016
  34. Malhotra , P. , Vig , L. , Shroff , G. , and Agarwal , P. Long Short Term Memory Networks for Anomaly Detection in Time Series 23rd European Symposium on Artificial Neural Networks Bruges, Belgium 2015 http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2015-56.pdf
  35. Munir , M. , Siddiqui , S.A. , Dengel , A. , and Ahmed , S. Deepant: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series IEEE Access 7 2018 1991 2005
  36. Buda , T.S. , Caglayan , B. , and Assem , H. Deepad: A Generic Framework Based on Deep Learning for Time Series Anomaly Detection Pacific-Asia Conference on Knowledge Discovery and Data Mining Delhi, India 577 588 2018
  37. Nanduri , A. and Sherry , L. Anomaly Detection in Aircraft Data Using Recurrent Neural Networks (RNN) 2016 Integrated Communications Navigation and Surveillance (ICNS) Herndon, VA 2016
  38. Hundman , K. , Constantinou , V. , Laporte , C. , Colwell , I. et al. Detecting Spacecraft Anomalies Using LSTMS and Nonparametric Dynamic Thresholding Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining London, UK 387 395 2018
  39. Kingma , D.P. and Welling , M. 2013
  40. Karl , M. , Soelch , M. , Bayer , J. , and van der Smagt , P. Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data International Conference on Learning Representations Toulon, France 2017
  41. Krishnan , R.G. , Shalit , U. , and Sontag , D. 2015
  42. Rissanen , J. Modeling by Shortest Data Description Automatica 14 5 1978 465 471
  43. Hinton , G.E. and Zemel , R.S. Autoencoders, Minimum Description Length and Helmholtz Free Energy Advances in Neural Information Processing Systems Denver CO 3 10 1994
  44. Theis , L. , van den Oord , A. , and Bethge , M. 2015
  45. Bowman , S.R. , Vilnis , L. , Vinyals , O. , Dai , A.M. et al. 2015
  46. Liu , X. , Gao , J. , Celikyilmaz , A. , Carin , L. et al. 2019
  47. Liang , S. , Li , Y. , and Srikant , R. 2017
  48. Hein , M. , Andriushchenko , M. , and Bitterwolf , J. Why Relu Networks Yield High-Confidence Predictions Far away from the Training Data and How to Mitigate the Problem Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Long Beach, CA 41 50 2019
  49. Lee , K. , Lee , H. , Lee , K. , and Shin , J. 2017
  50. Freedman , D. , Pisani , R. , and Purves , R. Statistics (International Student Edition) 4th New York WW Norton & Company 2007
  51. Alexander , R.A. A Note on Averaging Correlations Bulletin of the Psychonomic Society 28 4 1990 335 336
  52. Gal , Y. and Ghahramani , Z. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks Advances in Neural Information Processing Systems Barcelona, Spain 1019 1027 2016

Cited By