Abstract
Lane change maneuver on the highway is a complicated process. A quick and accurate decision for the maneuver is very important for a safe driving. This paper proposes a K-ELM (kernel extreme learning machine) based decision making method for mandatory lane changes. In this method, multiple driving variables that are essential for an accurate lane change are extracted and used as the inputs of an established K-ELM network to generate the right lane-changing decision. The K-ELM network is trained using a tenfold cross-validating approach with the vehicle trajectory data from the NGSIM (next generation simulation) data set on U.S. Highway 101 and Interstate 80. Simulation results demonstrate that the proposed method can generate the lane-changing decision with a 92.86% accuracy for merge events and a 94.36% accuracy for non-merge events. Compared with both the ELM and the SVM method, the proposed method is more accurate and faster in decision making.
Similar content being viewed by others
References
Toledo T, Koutsopoulos H, Ben-Akiva M (2003) Modeling integrated lane-changing behavior. Transp Res Rec J Transp Res Board 1857:30–38
Drew DR, LaMotte LR, Wattleworth JA et al (1967) Gap acceptance in the freeway merging process. Highw Res Rec 208:36
Gipps PG (1986) A model for the structure of lane-changing decisions. Transp Res Part B Methodol 20(5):403–414
Wiedemann R, Reiter U (1992) Microscopic traffic simulation: the simulation system MISSION, background and actual state. Proj ICARUS (V1052) Final Rep 2:1–53
Yang Q, Koutsopoulos HN (1996) A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transp Res Part C Emerg Technol 4(3):113–129
Sukthankar R, Baluja S, Hancock J (1997) Evolving an intelligent vehicle for tactical reasoning in traffic. In: Robotics and Automation, 1997. Proceedings., 1997 IEEE international conference on IEEE, vol 1, pp 519–524
Brackstone M, McDonald M, Wu J (1998) Lane changing on the motorway: factors affecting its occurrence, and their implications. In: Road transport information and control, 1998. 9th International conference on (Conf. Publ. no. 454). IET, pp 160–164
Hidas P (2005) Modelling vehicle interactions in microscopic simulation of merging and weaving. Transp Res Part C Emerg Technol 13(1):37–62
Schlenoff C, Madhavan R, Kootbally Z (2006) PRIDE: a hierarchical, integrated prediction framework for autonomous on-road driving. In: Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE international conference on IEEE, pp 2348–2353
Toledo T, Koutsopoulos HN, Ben-Akiva M (2007) Integrated driving behavior modeling. Transp Res Part C Emerg Technol 15(2):96–112
Dou Y, Yan F, Feng D (2016) Lane changing prediction at highway lane drops using support vector machine and artificial neural network classifiers. In: Advanced intelligent mechatronics (AIM), 2016 IEEE international conference on IEEE, pp 901–906
Rahman M, Chowdhury M, Xie Y et al (2013) Review of microscopic lane-changing models and future research opportunities. IEEE Trans Intell Transp Syst 14(4):1942–1956
Moridpour S, Sarvi M, Rose G (2010) Lane changing models: a critical review. Transp Lett 2(3):157–173
Ahmed KL, Ben-Akiva M, Koutsopoulos H et al (1996) Models of freeway lane changing and gap acceptance behavior. Transp Traffic Theory 13:501–515
Sharma A, Paliwal KK, Imoto S et al (2014) A feature selection method using improved regularized linear discriminant analysis. Mach Vis Appl 25(3):775–786
Sharma A, Paliwal KK, Imoto S et al (2013) Principal component analysis using QR decomposition. Int J Mach Learn Cybern 4(6):679–683
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cogn Model 5(3):1
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44(2):525–536
Huang GB, Zhou H, Ding X et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529
Sharma A, Paliwal KK (2015) Linear discriminant analysis for the small sample size problem: an overview. Int J Mach Learn Cybern 6(3):443–454
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant no. 61573075) and the Project of Standardization and New Model for Intelligent Manufacture (Grant no. 2016ZXFB06002).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cheng, S., Xu, Y., Zong, R. et al. A fast decision making method for mandatory lane change using kernel extreme learning machine. Int. J. Mach. Learn. & Cyber. 10, 3363–3369 (2019). https://doi.org/10.1007/s13042-019-00923-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-019-00923-8