Lane-Changing Intention Identification on Highway

Accurate and efficient lane change intention recognition is a prerequisite for safe driving warning, which can help eliminate the interference of vehicle lateral swing and reduce the false alarm rate of the warning system. Based on the Support Vector Machine (SVM), this paper identified the driver's lane change intention through analyzing the driving characteristics and laws before lane changing. First, 13 feature vectors that can represent lane change intentions were selected, and the dimensionality reduction of each feature is conducted by the Principal Component Analysis method. At the same time, the Grid Search Method was used to find the optimal parameters. After model training, the Area under Curve (AUC) of Receiver Operating Characteristic (ROC) curve was applied to verify the model accuracy specifically. Then, the paper analyzed the phenomenon of "intention cancellation" in lane changing process, explaining the misclassification results of partial lane keeping as lane changing. What grasped the period when lane changing intention occurs and improved precisely the performance of model recognition. Finally, vehicle trajectory data collected from I-80 expressway of NGSIM was used to verify the accuracy of this model. The results show that the model has a good recognition function and can provide some theoretical support for the development of the vehicle lane change model and vehicle early warning system on the expressway in China.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 20p

Subject/Index Terms

Filing Info

  • Accession Number: 01763938
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02625
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:15AM