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Personalized detection of lane changing behavior using multisensor data fusion

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Abstract

Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this paper, a fusion approach is introduced that utilizes multiple differing modality data, such as video data, GPS data, wheel odometry data, potentially IMU data collected from data logging device (DL1 MK3) for detecting driver’s behavior of lane changing by using a novel dimensionality reduction model, collaborative representation optimized projection classifier (CROPC). The criterion of CROPC is maximizing the collaborative representation based between-class scatter and minimizing the collaborative representation based within-class scatter in the transformed space simultaneously. For lane change detection, both feature-level fusion and decision-level fusion are considered. In the feature-level fusion, features generated from multiple differing modality data are merged before classification while in the decision-level fusion, an improved Dempster–Shafer theory based on correlation coefficient, DST-CC is presented to combine the classification outcomes from two classifiers, each corresponding to one kind of the data. The results indicate that the introduced fusion approach using a CROPC performs significantly better in terms of detection accuracy, in comparison to other state-of-the-art classifiers.

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References

  1. D’Agostino C, Saidi A, Scouarnec G, Chen L (2015) Learning-based driving events recognition and its application to digital roads. IEEE Trans Intell Trans Syst 16(4):2155–2166

    Article  Google Scholar 

  2. McCall J, Trivedi M (2016) Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans Intell Trans Syst 7(1):20–37

    Article  Google Scholar 

  3. Kasper D, Weidl G, Dang T et al (2012) Object-oriented Bayesian networks for detection of lane change maneuvers. In: Intelligent vehicles symposium. IEEE, pp 673–678

  4. Huang AS, Moore D, Antone M, Olson E, Teller S (2009) Finding multiple lanes in urban road networks with vision and lidar. Auton Robot 26(2):103–122

    Article  Google Scholar 

  5. Gu X, Zang A, Huang X et al (2015) Fusion of color images and LiDAR data for lane classification. In: 23rd Sigspatial international conference on advances in geographic information systems. ACM, p 69

  6. Tran D, Sheng W, Liu L et al (2015) A hidden Markov model based driver intention prediction system. In: International conference on cyber technology in automation, control, and intelligent systems. IEEE, pp 115–120

  7. Jin L, Hou H, Jiang Y (2012) Driver intention recognition based on continuous hidden Markov model. In: International conference on transportation, mechanical, and electrical engineering. IEEE, pp 739–742

  8. Zheng Y, Hansen John HL (2017) Lane-change detection from steering signal using spectral segmentation and learning-based classification. IEEE Trans Intell Veh 2(1):14–24

    Article  Google Scholar 

  9. Morris B, Doshi A, Trivedi M (2011) Lane change intent prediction for driver assistance: on-road design and evaluation. In: Intelligent vehicles symposium. IEEE, pp 895–901

  10. Kim IH, Bong JH, Park J et al (2017) Prediction of driver’s intention of lane change by augmenting sensor information using machine learning techniques. Sensors 17(6):1350

    Article  Google Scholar 

  11. Ramyar S, Homaifar A, Karimoddini A, et al (2016) Identification of anomalies in lane change behavior using one-class SVM. In: International conference on systems, man, and cybernetics. IEEE, pp 4405–4410

  12. Hou Y, Edara P, Sun C (2014) Modeling mandatory lane changing using bayes classifier and decision trees. IEEE Trans Intell Transp Syst 15(2):647–655

    Article  Google Scholar 

  13. Schubert R, Schulze K, Wanielik G (2010) Situation assessment for automatic lane-change maneuvers. IEEE Trans Intell Transp Syst 11(3):607–616

    Article  Google Scholar 

  14. Ulbrich S, Maurer M (2015) Situation assessment in tactical lane change behavior planning for automated vehicles. In: IEEE international conference on intelligent transportation systems, pp 975–981

  15. Yan F, Eilers M, Ludtke A et al (2016) Developing a model of driver’s uncertainty in lane change situations for trustworthy lane change decision aid systems. In: Intelligent vehicles symposium. IEEE, pp 406–411

  16. Peng J, Guo Y, Fu R, Yuan W, Wang C (2015) Multiparameter prediction of drivers’ lane-changing behaviour with neural network model. Appl Ergon 50:207–217

    Article  Google Scholar 

  17. Tomar RS, Verma S (2013) Lane change trajectory prediction using artificial neural network. Int J Veh Saf 6(3):213–234

    Article  Google Scholar 

  18. Leonhardt V, Wanielik G (2017) Feature evaluation for lane change prediction based on driving situation and driver behavior. In: IEEE 20th international conference on information fusion, pp 1–7

  19. Li J, Mei X, Prokhorov D et al (2017) Deep neural network for structural prediction and lane detection in traffic scene. IEEE Trans Neural Netw Learn Syst 28(3):690–703

    Article  Google Scholar 

  20. Olabiyi O, Martinson E, Chintalapudi V, et al (2017) Driver action prediction using deep (bidirectional) recurrent neural network. arXiv:1706.02257

  21. lexandru G, Tejaswi K, Smita V, et al (2016) DeepLanes: end-to-end lane position estimation using deep neural networks. In: IEEE conference on computer vision and pattern recognition, pp 38–45

  22. Kin J, Lee M (2014) Robust lane detection based on convolutional neural network and random sample consensus. In: International conference on neural information processing. pp 454–461

  23. Hanwool Woo et al (2017) Lane-change detection based on vehicle-trajectory prediction. IEEE Robot Autom Lett 2(2):1109–1116

    Article  Google Scholar 

  24. Yao W, Zhao H, Bonnifait P, Zha H (2013) Lane change trajectory prediction by using recorded human driving data. In: Intelligent vehicles symposium. IEEE, pp 430–436

  25. Nilsson J, Brannstrom M, Coelingh E et al (2017) Lane change maneuvers for automated vehicles. IEEE Trans Intell Transp Syst 18(5):1087–1096

    Article  Google Scholar 

  26. Xu G, Liu L, Ou Y et al (2012) Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction. IEEE Trans Intell Transp Syst 13(3):1138–1155

    Article  Google Scholar 

  27. Kim HT, Song B, Lee H et al (2015) Multiple vehicle recognition based on radar and vision sensor fusion for lane change assistance. J Inst Control 21(2):121–129

    Google Scholar 

  28. Cao G, Damerow F, Flade B et al (2016) Camera to map alignment for accurate low-cost lane-level scene interpretation. In: 19th international conference on intelligent transportation systems. IEEE, pp 498–504

  29. Satzoda RK, Trivedi MM (2015) Drive analysis using vehicle dynamics and vision-based lane semantics. IEEE Trans Intell Transp Syst 16(1):9–18

    Article  Google Scholar 

  30. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: IEEE international conference on computer vision, pp 471–478

  31. Chen C, Li W, Tramel EW et al (2014) Reconstruction of hyperspectral imagery from random projections using multihypothesis prediction. IEEE Trans Geosci Remote Sens 52(1):365–374

    Article  Google Scholar 

  32. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton

    MATH  Google Scholar 

  33. Jiang W (2016) A correlation coefficient of belief functions. arXiv:1612.05497

  34. http://www.race-technology.com/

  35. Gao J, Murphey YL, Zhu HH (2018) Detection of lane-changing behavior using collaborative representation classifier-based sensor fusion. In: SAE international journal of transportation safety, vol 6, no. 2. https://doi.org/10.4271/09-06-02-0010

  36. Chi Y, Porikli F (2012) Connecting the dots in multi-class classification: From nearest subspace to collaborative representation. In: IEEE international conference on computer vision and pattern recognition, pp 3602–3609

  37. Cai S, Zhang L, Zuo W et al (2016) A probabilistic collaborative representation based approach for pattern classification. In: IEEE international conference on computer vision and pattern recognition, pp 2950–2959

  38. Xu Y, Du Q, Li W et al (2017) Low-complexity multiple collaborative representations for hyperspectral image classification. In: International society for optics and photonics on high-performance computing in geoscience and remote sensing VII, vol 10430, p 1043003

  39. Brown G, Pocock A, Zhao MJ et al (2012) Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res 13(1):27–66

    MathSciNet  MATH  Google Scholar 

  40. Lu CY, Huang DS (2013) Optimized projections for sparse representation based classification. Neurocomputing 113:213–219

    Article  Google Scholar 

  41. Gao Q, Huang Y, Zhang H et al (2015) Discriminative sparsity preserving projections for image recognition. Pattern Recognit 48(8):2543–2553

    Article  Google Scholar 

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Acknowledgements

This research is supported in part by Research Grants from Toyota Research Institute (TRI).

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Authors and Affiliations

  1. School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430063, China

    Jun Gao & Honghui Zhu

  2. Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, 48128, USA

    Jun Gao & Yi Lu Murphey

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  1. Jun Gao
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  2. Yi Lu Murphey
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  3. Honghui Zhu
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Correspondence to Jun Gao.

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Gao, J., Murphey, Y.L. & Zhu, H. Personalized detection of lane changing behavior using multisensor data fusion. Computing 101, 1837–1860 (2019). https://doi.org/10.1007/s00607-019-00712-9

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  • DOI: https://doi.org/10.1007/s00607-019-00712-9

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