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

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 17/07/2018, Accepted 26/07/2018, Published 16/08/2018

The accurate prediction of the pedestrian trajectory is necessary to endow automatic guided vehicle with the capabilities to adjust velocity and path dynamically for the navigation in real pedestrian scenes. For this purpose, this study presents a social conscious prediction model considering two main factors that affect the pedestrians’ walking in the crowd – relative distance and moving direction. To form an effective model, the authors’ conscious pooling layer is added to the Long Shot Term Memory network (LTSM) model to build the relationship between pedestrians, learning the current position m and movement trend. The experiments are conducted to compare the proposed model with the previous state-of-the-art model on several public datasets. The experimental results show that the proposed model predicts pedestrian trajectories more accurately.

Inspec keywords: pedestrians; trajectory control; automatic guided vehicles; recurrent neural nets; control engineering computing; learning (artificial intelligence)

Other keywords: pedestrian scenes; human trajectory prediction; social conscious prediction model; LSTM model; pedestrian trajectory; automatic guided vehicle; recurrent neural network

Subjects: Neural computing techniques; Transportation system control; Knowledge engineering techniques; Mobile robots; Control engineering computing; Spatial variables control; Traffic engineering computing

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. 6175–6182.
    2. 2)
      • 21. Graves, A.: ‘Generating sequences with recurrent neural networks’, arXiv preprint arXiv:1308.0850, 2013.
    3. 3)
      • 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. 261–268.
    4. 4)
      • 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.
    5. 5)
      • 5. Treuille, A., Cooper, S., Popović, Z.: ‘Continuum crowds’. ACM Trans. Graph. (TOG), 2006, 25, (3), pp. 1160–1168.
    6. 6)
      • 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. 6000–6004.
    7. 7)
      • 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. 2153–2160.
    8. 8)
      • 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. 5060–5064.
    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. 312–319.
    10. 10)
      • 7. Tay, M.K.C., Laugier, C.: ‘Modelling smooth paths using Gaussian processes’. Field and Service Robotics, Berlin, Heidelberg, 2008, pp. 381–390.
    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)
      • 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. 426–433.
    13. 13)
      • 22. Dauphin, Y., de Vries, H., Bengio, Y.: ‘Equilibrated adaptive learning rates for non-convex optimization’, Adv. Neural Inf. Process. Syst., 2015, pp. 1504–1512.
    14. 14)
      • 24. Lerner, A., Chrysanthou, Y., Lischinski, D.: ‘Crowds by example’, Comput. Graph. Forum, Blackwell Publishing Ltd., 2007, 26, (3), pp. 655–664.
    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. 530–539.
    16. 16)
      • 17. Hochreiter, S., Schmidhuber, J.: ‘Long short-term memory’, Neural Comput., 1997, 9, (8), pp. 1735–1780.
    17. 17)
      • 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. 271–288.
    18. 18)
      • 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. 467–474.
    19. 19)
      • 18. Gregor, K., Danihelka, I., Graves, A., et al: ‘DRAW: a recurrent neural network for image generation’, Comput. Sci., 2015, pp. 1462–1471.
    20. 20)
      • 3. Helbing, D., Molnar, P.: ‘Social force model for pedestrian dynamics’, Phys. Rev. E, 1995, 51, (5), p. 4282.
    21. 21)
      • 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. 113–117.
    22. 22)
      • 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. 961–971.
    23. 23)
      • 8. Ballan, L., Castaldo, F., Alahi, A., et al: ‘Knowledge transfer for scene-specific motion prediction’. European Conf. Computer Vision, Cham, 2016, pp. 697–713.
    24. 24)
      • 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.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8264
Loading

Related content

content/journals/10.1049/joe.2018.8264
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading