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access icon openaccess Human trajectory prediction for automatic guided vehicle with recurrent neural network

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References

    1. 1)
      • 1. Pérez-D'Arpino, C., Shah, J.A.: ‘Fast target prediction of human reaching motion for cooperative human–robot manipulation tasks using time series classification’. 2015 IEEE Int. Conf. Robotics and Automation (ICRA), Seattle, USA, May 2015, pp. 61756182.
    2. 2)
      • 2. Waytz, A., Heafner, J., Epley, N.: ‘The mind in the machine: anthropomorphism increases trust in an autonomous vehicle’, J. Exp. Soc. Psychol., 2014, 52, pp. 113117.
    3. 3)
      • 3. Helbing, D., Molnar, P.: ‘Social force model for pedestrian dynamics’, Phys. Rev. E, 1995, 51, (5), p. 4282.
    4. 4)
      • 4. Johansson, A., Helbing, D., Shukla, P.K.: ‘Specification of the social force pedestrian model by evolutionary adjustment to video tracking data’, Adv. Complex Syst., 2007, 10, (supp02), pp. 271288.
    5. 5)
      • 5. Treuille, A., Cooper, S., Popović, Z.: ‘Continuum crowds’. ACM Trans. Graph. (TOG), 2006, 25, (3), pp. 11601168.
    6. 6)
      • 6. Trautman, P., Ma, J., Murray, R.M., et al: ‘Robot navigation in dense human crowds: the case for cooperation’. 2013 IEEE Int. Conf. Robotics and Automation (ICRA), Karlsruhe, Germany, May 2013, pp. 21532160.
    7. 7)
      • 7. Tay, M.K.C., Laugier, C.: ‘Modelling smooth paths using Gaussian processes’. Field and Service Robotics, Berlin, Heidelberg, 2008, pp. 381390.
    8. 8)
      • 8. Ballan, L., Castaldo, F., Alahi, A., et al: ‘Knowledge transfer for scene-specific motion prediction’. European Conf. Computer Vision, Cham, 2016, pp. 697713.
    9. 9)
      • 9. Morris, B., Trivedi, M.: ‘Learning trajectory patterns by clustering: experimental studies and comparative evaluation’. 2009 IEEE Conf. Computer Vision and Pattern Recognition, Miami, Florida, June 2009, pp. 312319.
    10. 10)
      • 10. Alahi, A., Goel, K., Ramanathan, V., et al: ‘Social LSTM: human trajectory prediction in crowded spaces’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, USA, June 2016, pp. 961971.
    11. 11)
      • 11. Luong, M.T., Pham, H., Manning, C.D.: ‘Effective approaches to attention-based neural machine translation’, arXiv preprint arXiv:1508.04025, 2015.
    12. 12)
      • 12. Cho, K., Merrienboer, B.V., Gulcehre, C., et al: ‘Learning phrase representations using RNN encoder–decoder for statistical machine translation’, arXiv preprint arXiv:1406.1078, 2014.
    13. 13)
      • 13. Lu, L., Zhang, X., Renais, S.: ‘On training the recurrent neural network encoder–decoder for large vocabulary end-to-end speech recognition’. 2016 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016, pp. 50605064.
    14. 14)
      • 14. Chen, X., Liu, X., Qian, Y., et al: ‘CUED-RNNLM – an open-source toolkit for efficient training and evaluation of recurrent neural network language models’. 2016 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016, pp. 60006004.
    15. 15)
      • 15. Mesnil, G., Dauphin, Y., Yao, K., et al: ‘Using recurrent neural networks for slot filling in spoken language understanding’, IEEE/ACM Trans. Audio Speech Lang. Process., 2015, 23, (3), pp. 530539.
    16. 16)
      • 16. Mikolov, T., Karafiát, M., Burget, L., et al: ‘Recurrent neural network based language model’. 11th Annual Conf. Int. Speech Communication Association, Chiba, Japan, September 2010.
    17. 17)
      • 17. Hochreiter, S., Schmidhuber, J.: ‘Long short-term memory’, Neural Comput., 1997, 9, (8), pp. 17351780.
    18. 18)
      • 18. Gregor, K., Danihelka, I., Graves, A., et al: ‘DRAW: a recurrent neural network for image generation’, Comput. Sci., 2015, pp. 14621471.
    19. 19)
      • 19. Visin, F., Romero, A., Cho, K., et al: ‘Reseg: a recurrent neural network-based model for semantic segmentation’. 2016 IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, USA, June 2016, pp. 426433.
    20. 20)
      • 20. Ebrahimi Kahou, S., Michalski, V., Konda, K., et al: ‘Recurrent neural networks for emotion recognition in video’. Proc. 2015 ACM on Int. Conf. Multimodal Interaction, Seattle, USA, November 2015, pp. 467474.
    21. 21)
      • 21. Graves, A.: ‘Generating sequences with recurrent neural networks’, arXiv preprint arXiv:1308.0850, 2013.
    22. 22)
      • 22. Dauphin, Y., de Vries, H., Bengio, Y.: ‘Equilibrated adaptive learning rates for non-convex optimization’, Adv. Neural Inf. Process. Syst., 2015, pp. 15041512.
    23. 23)
      • 23. Pellegrini, S., Ess, A., Schindler, K., et al: ‘You'll never walk alone: modeling social behavior for multi-target tracking’. Computer Vision 2009 IEEE 12th Int. Conf., 2009, pp. 261268.
    24. 24)
      • 24. Lerner, A., Chrysanthou, Y., Lischinski, D.: ‘Crowds by example’, Comput. Graph. Forum, Blackwell Publishing Ltd., 2007, 26, (3), pp. 655664.
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