A fast decision making method for mandatory lane change using kernel extreme learning machine

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.

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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).

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Correspondence to Senlin Cheng.

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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

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Keywords

  • Lane-changing opportunity
  • K-ELM algorithm
  • Mandatory lane changing
  • Feature selection
  • Highway merging area