Online Identification of Low-Speed Misfit Driving Behavior based on Fuzzy Comprehensive Evaluation Method and XGBoost Algorithm

Timely identification of low-speed misfit driving behavior is essential for enhancing road traffic safety and operational efficiency. This paper proposes an online recognition method for detecting such behavior. First, eight characteristic indicators are selected and quantified from the perspectives of the vehicle itself and the vehicles ahead and behind. Then, thresholds are determined using the interquartile range method, and indicator weights are calculated using the CRITIC method. A membership function is constructed to perform a fuzzy comprehensive evaluation and determine the behavioral state of the vehicle. Finally, the XGBoost algorithm is applied to train an online recognition model using the feature indicators and evaluation results, with the model validated on the highD dataset. The results demonstrate that the fuzzy comprehensive evaluation method, based on the eight defined indicators, effectively identifies low-speed misfit vehicles. The XGBoost model further enhances recognition accuracy. This research provides valuable insights for transportation authorities in managing vehicle behavior and maintaining efficient and stable traffic flow. It also holds practical significance for alleviating congestion and ensuring unimpeded access for critical services such as ambulances and disaster response vehicles.