Detection of Frequent Lane-Change Behavior Using Smart Phone Inertial Sensor Data

Frequent lane-change of vehicles can have a serious impact on traffic safety and traffic congestion. In the era of the internet of vehicles, it is helpful to regulate driver’s driving behavior by detecting frequent lane-change behaviors and promptly alerting the driver. In this paper, the accelerometer and gyroscope data collected by the smartphone inertial sensor is being used to analyze the lane-change behavior of the vehicles. The data is being trained by AdaBoost and combined with the sliding time window for frequency identification. The effectiveness of the proposed algorithm is being compared to that of SVM algorithm and BP neural network. The results show that AdaBoost algorithm is superior to SVM algorithm and BP neural network. Finally, the same experiment was conducted with the data collected by the high-precision inertial at the same time. When compared with the results of Adaboost algorithm, the practicability of the proposed method is further verified.


  • English

Media Info

  • Media Type: Web
  • Pagination: pp 401-410
  • Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

Filing Info

  • Accession Number: 01767333
  • Record Type: Publication
  • ISBN: 9780784483053
  • Files: TRIS, ASCE
  • Created Date: Dec 9 2020 3:01PM