Skip to main content
SpringerLink
Log in

Highway Lane-Changing Prediction Using a Hierarchical Software Architecture based on Support Vector Machine and Continuous Hidden Markov Model

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Lane changing behavior is one of the most essential and complex driving attributes. The lack of proper lane changing behavior can lead to collisions and traffic congestion. In this work, a novel hierarchical software architecture for the prediction of lane changing behavior on highways has been developed and evaluated. The two-layer hierarchical structure of the proposed model is based on a support vector machine (SVM) in the first layer followed by another model based on continuous Hidden Markov Model (HMM) incorporated with a Gaussian Mixture Model (GMM) in the second layer. The trajectory classification predicted in the first layer by the SVM is binary, i.e., Lane Change (LC) and Lane Keep (LK) behaviors. The second layer of the software architecture further classifies the LC behavior output of the first layer to left-lane change (LLC) and right-lane change (RLC) behaviors using the model of continuous HMM (CHMM) incorporated with GMM. The developed model has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration’s Next Generation Simulation (NGSIM). The first layer prediction is performed within an approximately 10 seconds time window. The positions, velocity and Time to Collision (TTC) of the target and surrounding vehicles are taken as input parameters in the model execution of the second layer. The test results show that the proposed hierarchical model exhibits 91% accuracy for LLC, 87% accuracy for RLC and 99% accuracy for LK behaviors. This model can be effectively used as a lane changing suggestion system in the advanced driver assistance systems (ADAS).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Horiguchi, R., Oguchi, T.: A study on car following models simulating various adaptive cruise control behaviors. Int. J. Intell. Transp. Syst. Res. 12(3), 127–134 (2014)

    Google Scholar 

  2. Nakamura, H., Yamabe, S., Nakano, K., Yamaguchi, D., Suda, Y.: Driver risk perception and physiological state during car-following experiments using a driving simulator. Int. J. Intell. Transp. Syst. Res. 8(3), 140–150 (2010)

    Google Scholar 

  3. Kasai, M., Xing, J.: Use of particle filtering to establish a time-varying car-following model. Int. J. Intell. Transp. Syst. Res. 17(1), 49–60 (2019)

    Google Scholar 

  4. Osafune, T., Takahashi, T., Kiyama, N., Sobue, T., Yamaguchi, H., Higashino, T.: Analysis of accident risks from driving behaviors. Int. J. Intell. Transp. Syst. Res. 15(3), 192–202 (2017)

    Google Scholar 

  5. Zheng, Z.: Recent developments and research needs in modeling lane changing. Transp. Res. Part B: Methodol. 60, 16–32 (2014)

    Article  Google Scholar 

  6. Yoshizawa, R., Shiomi, Y., Uno, N., Iida, K., Yamaguchi, M.: Analysis of car-following behavior on sag and curve sections at intercity expressways with driving simulator. Int. J. Intell. Transp. Syst. Res. 10(2), 56–65 (2012)

    Google Scholar 

  7. Jin, H., Duan, C., Liu, Y., Lu, P.: Gauss mixture hidden markov model to characterise and model discretionary lane-change behaviours for autonomous vehicles. IET Intell. Transp. Syst. 14(5), 401–411 (2020)

    Article  Google Scholar 

  8. Sathyanarayana, A., Boyraz, P., Hansen, J.H.: Driver behavior analysis and route recognition by hidden markov models. Proc. of IEEE International Conference on Vehicular Electronics and Safety, pp. 276–281 (2008)

  9. Zhao, Z., Wang, Y., Hu, X., Tao, Y., Wang, J.: Research on identification method of heavy vehicle rollover based on hidden markov model. Open Phys. 15(1), 479–485 (2017)

    Article  Google Scholar 

  10. Xia, Y., Qu, Z., Sun, Z., Li, Z.: A human-like model to understand surrounding vehicles lane changing intentions for autonomous driving. IEEE Transactions on Vehicular Technology (2021)

  11. Li, X., Wang, W., Roetting, M.: Estimating driver’s lane-change intent considering driving style and contextual traffic. IEEE Trans. Intell. Transp. Syst. 20(9), 3258–3271 (2018)

    Article  Google Scholar 

  12. Zheng, Y., Ding, W., Ran, B., Qu, X., Zhang, Y.: Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory-based lane change strategy. IET Intell. Transp. Syst. 14(13), 1864–1870 (2020)

    Article  Google Scholar 

  13. Ali, Y., Zheng, Z., Haque, M.M., Wang, M.: A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment. Transp. Res. Part C: Emerging Technol. 106, 220–242 (2019)

    Article  Google Scholar 

  14. Talebpour, A., Mahmassani, H.S., Hamdar, S.H.: Modeling lane-changing behavior in a connected environment: A game theory approach. Transp. Res. Procedia 7, 420–440 (2015)

    Article  Google Scholar 

  15. Ren, G., Zhang, Y., Liu, H., Zhang, K., Hu, Y.: A new lane-changing model with consideration of driving style. Int. J. Intell. Transp. Syst. Res. 17(3), 181–189 (2019)

    Google Scholar 

  16. Tomar, R.S., Verma, S., Tomar, G.S.: Prediction of lane change trajectories through neural network. Proc. of IEEE International Conference on Computational Intelligence and Communication Networks, pp. 249–253 (2010)

  17. Ding, C., Wang, W., Wang, X., Baumann, M.: A neural network model for driver’s lane-changing trajectory prediction in urban traffic flow. Math. Probl. Eng. 2013 (2013)

  18. Díaz-Álvarez, A., Clavijo, M., Jiménez, F., Talavera, E., Serradilla, F.: Modelling the human lane-change execution behaviour through multilayer perceptrons and convolutional neural networks. Transp. Res. Part F Traffic Psychol. Behav. 56, 134–148 (2018)

    Article  Google Scholar 

  19. Zheng, J., Suzuki, K., Fujita, M.: Predicting driver’s lane-changing decisions using a neural network model. Simul. Model. Pract. Theory 42, 73–83 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Xu, T., Zhang, Z., Wu, X., Qi, L., Han, Y.: Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps. Phys. A: Stat. Mech. Appl. 567, 125691 (2021)

    Article  Google Scholar 

  22. Zhang, Y., Xu, Q., Wang, J., Wu, K., Zheng, Z., Lu, K.: A learning-based discretionary lane-change decision-making model with driving style awareness.arXiv:2010.09533 (2020)

  23. Dou, Y., Fang, Y., Hu, C., Zheng, R., Yan, F.: Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway. IET Intell. Transp. Syst. 13(1), 48–54 (2018)

    Article  Google Scholar 

  24. Dou, Y., Yan, F., Feng, D.: Lane changing prediction at highway lane drops using support vector machine and artificial neural network classifiers. Proc. of IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 901–906 (2016)

  25. Huang, L., Guo, H., Zhang, R., Wang, H., Wu, J.: Capturing drivers’ lane changing behaviors on operational level by data driven methods. IEEE Access 6, 57497–57506 (2018)

    Article  Google Scholar 

  26. Xing, Y., Lv, C., Cao, D., Velenis, E.: Multi-scale driver behavior modeling based on deep spatialtemporal representation for intelligent vehicles. Transp. Res. Part C: Emerg. Technol. 130, 103288 (2021)

    Article  Google Scholar 

  27. Wei, C., Hui, F., Khattak, A.J.: Driver lane-changing behavior prediction based on deep learning. J. Adv. Transp. 2021 (2021)

  28. Mahajan, V., Katrakazas, C., Antoniou, C.: Prediction of lane-changing maneuvers with automatic labeling and deep learning. Transp. Res. Record 2674(7), 336–347 (2020)

    Article  Google Scholar 

  29. Xing, Y., Lv, C., Liu, Y.H., Zhao, Y., Cao, D., Kawahara, S.: Hybrid-learning-based driver steering intention prediction using neuromuscular dynamics. IEEE Trans. Ind. Electro. (2021)

  30. Wang, H., Yuan, S., Guo, M., Li, X., Lan, W.: A deep reinforcement learning-based approach for autonomous driving in highway on-ramp merge. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering p 0954407021999480 (2021)

  31. Das, S., Bowles, B.: Simulations of highway chaos using fuzzy logic. Proc. of 18th IEEE International Conference of the North American Fuzzy Information Processing Society-NAFIPS (Cat. No. 99TH8397), pp. 130–133 (1999)

  32. Hou, Y., Edara, P., Sun, C.: A genetic fuzzy system for modeling mandatory lane changing. Proc. of 15th IEEE International Conference on Intelligent Transportation Systems, pp. 1044–1048 (2012)

  33. Balal, E., Cheu, R. L., Sarkodie-Gyan, T.: A binary decision model for discretionary lane changing move based on fuzzy inference system. Transp. Res. Part C Emerg. Technol. 67, 47–61 (2016)

    Article  Google Scholar 

  34. Tang, J., Liu, F., Zhang, W., Ke, R., Zou, Y.: Lane-changes prediction based on adaptive fuzzy neural network. Expert Syst. Appl. 91, 452–463 (2018)

    Article  Google Scholar 

  35. Wang, W., Qie, T., Yang, C., Liu, W., Xiang, C., Huang, K.: An intelligent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle. IEEE Trans. Ind. Electron. (2021)

  36. Shi, W., Zhang, Y.P.: Decision analysis of lane change based on fuzzy logic. In: Applied Mechanics and Materials, vol. 419, pp 790–794. Trans Tech Publ (2013)

  37. Olsen, E. C. B.: Modeling slow lead vehicle lane changing. Ph.D. thesis, Virginia Tech (2003)

  38. Jin, W. L.: A kinematic wave theory of lane-changing traffic flow. Transp. Res. Part B Methodol 44(8-9), 1001–1021 (2010)

    Article  Google Scholar 

  39. Henning, M.J., Georgeon, O., Krems, J.F.: The quality of behavioral and environmental indicators used to infer the intention to change lanes (2007)

  40. Laval, J.A., Leclercq, L.: Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model. Transp. Res. B Methodol. 42(6), 511–522 (2008)

    Article  Google Scholar 

  41. Salvucci, D. D.: Modeling driver behavior in a cognitive architecture. Hum. Factors 48(2), 362–380 (2006)

    Article  Google Scholar 

  42. Baumann, M., Krems, J. F.: Situation awareness and driving: A cognitive model. In: Modelling driver behaviour in automotive environments, pp 253–265. Springer (2007)

  43. Li, Z., Wu, C., Tao, P., Tian, J., Ma, L.: Dp and ds-lcd: A new lane change decision model coupling driver’s psychology and driving style. IEEE Access 8, 132614–132624 (2020)

    Article  Google Scholar 

  44. Hou, Y., Edara, P., Sun, C.: Situation assessment and decision making for lane change assistance using ensemble learning methods. Expert Syst. Appl. 42(8), 3875–3882 (2015)

    Article  Google Scholar 

  45. Nilsson, J., Fredriksson, J., Coelingh, E.: Rule-based highway maneuver intention recognition. Proc. of 18th IEEE International Conference on Intelligent Transportation Systems, pp. 950–955 (2015)

  46. Choudhury, C. F., Ramanujam, V., Ben-Akiva, M. E.: Modeling acceleration decisions for freeway merges. Transp. Res. Record 2124(1), 45–57 (2009)

    Article  Google Scholar 

  47. Yeo, H., Skabardonis, A., Halkias, J., Colyar, J., Alexiadis, V.: Oversaturated freeway flow algorithm for use in next generation simulation. Transp. Res. Rec. 2088(1), 68–79 (2008)

    Article  Google Scholar 

  48. Coifman, B., Li, L.: A critical evaluation of the next generation simulation (ngsim) vehicle trajectory dataset. Transp. Res. B Methodol. 105, 362–377 (2017)

    Article  Google Scholar 

  49. Vassili Alexiadis, J. C., Halkias, J.: Next generation simulation fact sheet, washington, dc, usa. [online] available: ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm (2007)

  50. Wang, Q., Li, Z., Li, L.: Investigation of discretionary lane-change characteristics using next-generation simulation data sets. J. Intell. Transp. Syst. 18(3), 246–253 (2014)

    Article  Google Scholar 

  51. Punzo, V., Borzacchiello, M.T., Ciuffo, B.: On the assessment of vehicle trajectory data accuracy and application to the next generation simulation (ngsim) program data. Transp. Res. Part C: Emerg. Technol. 19(6), 1243–1262 (2011)

    Article  Google Scholar 

  52. Bosnak, M., Skrjanc, I.: Efficient time-to-collision estimation for a braking supervision system with LIDAR. Proc. of 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1–6 (2017)

  53. Dong, C., Wang, H., Li, Y., Shi, X., Ni, D., Wang, W.: Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp. Transportmetrica A: Transport Sci. 17(1), 124–150 (2021)

    Article  Google Scholar 

  54. Glaser, S., Vanholme, B., Mammar, S., Gruyer, D., Nouveliere, L.: Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Trans. Intell. Transp. Syst. 11(3), 589–606 (2010)

    Article  Google Scholar 

  55. Ward, J., Agamennoni, G., Worrall, S., Nebot, E.: Vehicle collision probability calculation for general traffic scenarios under uncertainty. In: IEEE Intelligent Vehicles Symposium Proceedings, pp 986–992 (2014)

  56. Kim, J., Kum, D.: Collision risk assessment algorithm via lane-based probabilistic motion prediction of surrounding vehicles. IEEE Trans. Intell. Transp. Syst. 19(9), 2965–2976 (2017)

    Article  Google Scholar 

  57. Xu, G., Liu, L., Ou, Y., Song, Z.: 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 (2012)

    Article  Google Scholar 

  58. Zhang, Y., Lin, Q., Wang, J., Verwer, S., Dolan, J.M.: Lane-change intention estimation for car-following control in autonomous driving. IEEE Trans. Intell. Veh. 3(3), 276–286 (2018)

    Article  Google Scholar 

  59. Minderhoud, M.M., Bovy, P. H.: Extended time-to-collision measures for road traffic safety assessment. Accid. Analy. Prev. 33(1), 89–97 (2001)

    Article  Google Scholar 

  60. Chen, T., Shi, X., Wong, Y.D.: Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data. Accid. Anal. Prev. 129, 156–169 (2019)

    Article  Google Scholar 

  61. Coelingh, E., Eidehall, A., Bengtsson, M.: Collision warning with full auto brake and pedestrian detection-a practical example of automatic emergency braking. Proc. of 13th International IEEE Conference on Intelligent Transportation Systems, pp. 155–160 (2010)

  62. Labayrade, R., Royere, C., Aubert, D.: A collision mitigation system using laser scanner and stereovision fusion and its assessment. Proc. of Intelligent Vehicles Symposium, pp. 441–446 (2005)

  63. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  64. Chen, S., Cowan, C.F., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2(2), 302–309 (1991)

    Article  Google Scholar 

  65. Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state markov chains. Ann. Math. Stat. 37(6), 1554–1563 (1966)

    Article  MathSciNet  MATH  Google Scholar 

  66. Rabiner, L., Juang, B.: An introduction to hidden markov models. IEEE assp Mag 3(1), 4–16 (1986)

    Article  Google Scholar 

  67. Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interd. Rev. Comput. Stat. 2 (4), 433–459 (2010)

    Article  Google Scholar 

  68. McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)

    Article  Google Scholar 

  69. Edwards, A.L.: Note on the “correction for continuity” in testing the significance of the difference between correlated proportions. Psychometrika 13(3), 185–187 (1948)

    Article  Google Scholar 

  70. Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)

    Article  Google Scholar 

  71. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J., DATA, M.: Practical machine learning tools and techniques. In: Data Mining (2), p 4 (2005)

  72. Cumming, G., Calin-Jageman, R.: Introduction to the new statistics: Estimation, open science, and beyond. Routledge (2016)

Download references

Acknowledgements

The research carried out in this work is supported by KPIT Technologies Pvt. Ltd., Bangalore, India, under the research grant for the project entitled “Driver Behavior Modelling for Autonomous Driving”.

Author information

Authors and Affiliations

  1. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India

    Omveer Sharma, N. C. Sahoo & N. B. Puhan

Authors
  1. Omveer Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. C. Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. B. Puhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omveer Sharma.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, O., Sahoo, N.C. & Puhan, N.B. Highway Lane-Changing Prediction Using a Hierarchical Software Architecture based on Support Vector Machine and Continuous Hidden Markov Model. Int. J. ITS Res. 20, 519–539 (2022). https://doi.org/10.1007/s13177-022-00308-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-022-00308-2

Keywords

Search

Navigation