Driving Maneuvers Recognition and Classification Using A Hyprid Pattern Matching and Machine Learning

Since most of the road and traffic accidents are related to human errors or distraction, the study of irregular driving behaviors is considered one of the most important research topics in this field. To prevent road accidents and assess driving competencies, there is an urgent need to evaluate driving behavior through the design of a driving maneuvers assessment system. In this study, the recognition and classification of highway driving maneuvers using smartphones’ build-in sensors are presented. The paper examines the performance of three classical machine learning techniques and a novel hybrid system. The proposed hybrid system combines the pattern machining Dynamic Time Warping (DTW) technique for recognizing driving maneuvers and the machine learning techniques for classification. Results obtained from both approaches show that the performance of the hybrid system is superior to that obtained by using classical machine learning techniques. This enhancement in the performance of the hybrid system is due to the elimination of the overlapping in the target classes due to the separation, the recognition and the classification processes.

@article{Al-Din2023,
  title     = {Driving Maneuvers Recognition and Classification Using A Hyprid Pattern Matching and Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {2},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Munaf Salim Najim Al-Din},
  doi       = {10.14569/IJACSA.2023.0140230},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140230}
}