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Sensor fusion for vehicle tracking based on the estimated probability

Sensor fusion for vehicle tracking based on the estimated probability

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The track-to-track fusion (T2TF) algorithm is currently an attractive fusion methodology in the industry because the algorithm can reflect the reliability of sensor tracks. However, the T2TF algorithm cannot be applied when the probability information of the sensor is unknown. The aim of this study is to exploit the T2TF algorithm even in the absence of the probability information of the sensor. The covariance is estimated using the recursive equations of the Kalman filter. In addition, a novel track-association approach using the total similarity is developed to improve association performance. The total similarity complements the defects of the track disposition and the estimated track history. Finally, by fusing the associated tracks using the estimated covariance, the T2TF algorithm is successfully applied to sensors with an unknown covariance. The fusion results are then evaluated using the correct association rate and the optimal subpattern assignment metric. The simulation results obtained show the superiority of the proposed algorithm under three scenarios.

References

    1. 1)
      • 1. Aeberhard, M., Schlichtharle, S., Kaempchen, N., et al: ‘Track-to-track fusion with asynchronous sensors using information matrix fusion for surround environment perception’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (4), pp. 17171726.
    2. 2)
      • 2. Ziebinski, A., Cupek, R., Erdogan, H., et al: ‘A survey of ADAS technologies for the future perspective of sensor fusion’. Int. Conf. on Computational Collective Intelligence, Halkidiki, Greece, 2016, pp. 135146.
    3. 3)
      • 3. Gietelink, O., Ploeg, J., Schutter, B.D., et al: ‘Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations’, Veh. Syst. Dyn., 2006, 44, (7), pp. 569590.
    4. 4)
      • 4. Xiao, L., Gao, F.: ‘A comprehensive review of the development of adaptive cruise control systems’, Veh. Syst. Dyn., 2010, 48, (10), pp. 11671192.
    5. 5)
      • 5. Marzbanrad, J., Zadeh Moghaddam, I.T.: ‘Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade’, Veh. Syst. Dyn., 2016, 54, (9), pp. 12911316.
    6. 6)
      • 6. Lu, M., Wevers, K., Heijden, R.V.D.: ‘Technical feasibility of advanced driver assistance systems (ADAS) for road traffic safety’, Transp. Plan. Technol., 2005, 28, (3), pp. 167187.
    7. 7)
      • 7. Bengler, K., Dietmayer, K., Farber, B., et al: ‘Three decades of driver assistance systems: review and future perspectives’, IEEE Intell. Transp. Syst. Mag., 2014, 6, (4), pp. 622.
    8. 8)
      • 8. Musleh, B., García, F., Otamendi, J., et al: ‘Identifying and tracking pedestrians based on sensor fusion and motion stability predictions’, Sensors, 2010, 10, (9), pp. 80288053.
    9. 9)
      • 9. Sivaraman, S., Trivedi, M.M.: ‘Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (4), pp. 17731795.
    10. 10)
      • 10. Alessandretti, G., Broggi, A., Cerri, P.: ‘Vehicle and guard rail detection using radar and vision data fusion’, IEEE Trans. Intell. Transp. Syst., 2007, 8, (1), pp. 95105.
    11. 11)
      • 11. Santoso, F., Garratt, M.A., Anavatti, S.G.: ‘Visual-inertial navigation systems for aerial robotics: sensor fusion and technology’, IEEE Trans. Autom. Sci. Eng., 2017, 14, (1), pp. 260275.
    12. 12)
      • 12. Mori, S., Barker, W.H., Chong, C.Y., et al: ‘Track association and track fusion with nondeterministic target dynamics’, IEEE Trans. Aerosp. Electron. Syst., 2002, 38, (2), pp. 659668.
    13. 13)
      • 13. Willsky, A., Bello, M., Castanon, D., et al: ‘Combining and updating of local estimates and regional maps along sets of one-dimensional tracks’, IEEE Trans. Autom. Control, 1982, 27, (4), pp. 799813.
    14. 14)
      • 14. Lobbia, R., Kent, M.: ‘Data fusion of decentralized local tracker outputs’, IEEE Trans. Aerosp. Electron. Syst., 1994, 30, (3), pp. 787799.
    15. 15)
      • 15. Tian, W., Wang, Y., Shan, X., et al: ‘Track-to-track association for biased data based on the reference topology feature’, IEEE Signal Process. Lett., 2014, 21, (4), pp. 449453.
    16. 16)
      • 16. Schuhmacher, D., Vo, B.T., Vo, B.N.: ‘A consistent metric for performance evaluation of multi-object filters’, IEEE Trans. Signal Process., 2008, 56, (8), pp. 34473457.
    17. 17)
      • 17. Bar-Shalom, Y., Tian, X., Willett, P.K.: ‘Tracking and data fusion: a handbook of algorithms’ (YBS Publishing, USA, CT, Bloomfield, 2011).
    18. 18)
      • 18. Houenou, A., Bonnifait, P., Cherfaoui, V., et al: ‘A track-to-track association method for automotive perception systems’. IEEE Intelligent Vehicles Symp. (IV) (IEEE, 2012), Alcala de Henares, Spain, 2012, pp. 704710.
    19. 19)
      • 19. Li, X.R., Jilkov, V.P.: ‘Survey of maneuvering target tracking. Part I. Dynamic models’, IEEE Trans. Aerosp. Electron. Syst., 2003, 39, (4), pp. 13331364.
    20. 20)
      • 20. Bar-Shalom, Y., Li, X.R.: ‘Estimation and tracking- principles, techniques, and software’ (Artech House, Inc., Norwood, MA, 1993).
    21. 21)
      • 21. Rangesh, A., Trivedi, M.M.: ‘No blind spots: full-surround multi-object tracking for autonomous vehicles using cameras & LiDARs’, 2018, arXiv preprint arXiv:180208755.
    22. 22)
      • 22. Eum, S., Jung, H.G.: ‘Enhancing light blob detection for intelligent headlight control using lane detection’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 10031011.
    23. 23)
      • 23. Jung, H., Lee, Y., Kang, H., et al: ‘Sensor fusion-based lane detection for lks + acc system’, Int. J. Autom. Technol., 2009, 10, (2), pp. 219228.
    24. 24)
      • 24. Mikulski, J.: ‘Activities of transport telematics’ (Springer-Verlag, Berlin, Heidelberg, 2013).
    25. 25)
      • 25. Do, Q.H., Tehrani, H., Mita, S., et al: ‘Human drivers based active-passive model for automated lane change’, IEEE Intell. Transp. Syst. Mag., 2017, 9, (1), pp. 4256.
    26. 26)
      • 26. Miron, A., Ainouz, S., Rogozan, A., et al: ‘Cross-comparison census for colour stereo matching applied to intelligent vehicle’, Electron. Lett., 2012, 48, (24), pp. 15301532.
    27. 27)
      • 27. Lee, C.J., Pak, J.M., Ahn, C.K., et al: ‘Multi-target FIR tracking algorithm for Markov jump linear systems based on true-target decision making’, Neurocomputing, 2015, 168, (Supplement C), pp. 298307.
    28. 28)
      • 28. Dong, P., Jing, Z., Gong, D., et al: ‘Maneuvering multi-target tracking based on variable structure multiple model GMCPHD filter’, Signal Process., 2017, 141, pp. 158167.
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