Your browser does not support JavaScript!

Structured behaviour prediction of on-road vehicles via deep forest

Structured behaviour prediction of on-road vehicles via deep forest

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Vision-based vehicle behaviour analysis has drawn increasing research efforts as an interesting and challenging issue in recent years. Although a variety of approaches have been taken to characterise on-road behaviour, there still lacks a general model for interpreting the behaviour of vehicles on the road. In this Letter, the authors propose a new method that effectively predicts the vehicle behaviour based on structured deep forest modelling. Inspired by structured learning, the structure information of vehicle behaviour is extracted from the detected vehicle, and then the corresponding structured label is constructed. Especially, the structured label visually expresses the vehicle behaviour as contrast to the discrete numerical label. With the structured label, a structured deep forest model is proposed to predict the vehicle behaviour. Experimental results illustrate that the proposed method successfully obtains the implication of semantic interpretation of vehicle behaviour by the predicted structured labels, and meanwhile it achieves comparable performance with traditional methods.


    1. 1)
    2. 2)
      • 7. Sivaraman, S., Morris, B.T., Trivedi, M.M.: ‘Learning multi-lane trajectories using vehicle-based vision’. Proc. IEEE Int. Conf. Comput. Vision Workshop, Barcelona, Spain, November 2011, pp. 20702076.
    3. 3)
    4. 4)
      • 15. Wiest, J., Hoffken, M., Kresel, U., et al: ‘Probabilistic trajectory prediction with Gaussian mixture models’. Proc. IEEE IV Symp., Alcala de Henares, Spain, June 2012, pp. 141146.
    5. 5)
    6. 6)
      • 14. Shirazi, M.S., Morris, B.: ‘Contextual combination of appearance and motion for intersection videos with vehicles and pedestrians’, International Symposium on Visual Computing (ISVC), Las Vegas, NV, USA, December 2014, pp. 708717.
    7. 7)
      • 9. Hermes, C., Wohler, C., Schenk, K., et al: ‘Long-term vehicle motion prediction’. Proc. IEEE Intelligent Vehicles Symp., Xi'an, China, June 2009, pp. 652657.
    8. 8)
      • 6. Barth, A., Franke, U.: ‘Tracking oncoming and turning vehicles at intersections’. Proc. 13th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), Funchal, Portugal, September 2010, pp. 861868.
    9. 9)
    10. 10)
    11. 11)
      • 13. Zhou, Z.H., Feng, J.‘Deep forest: Towards an alternative to deep neural networks’, 2017, arXiv preprint arXiv:1702.08835.
    12. 12)
    13. 13)
    14. 14)
    15. 15)

Related content

This is a required field
Please enter a valid email address