Directional lane change prediction using machine learning methods

Abstract

This research employs a series of machine learning methods to predict the direction of lane change. The response is a binary variable indicating changing the lane to the left or to the right. The employed methods include Decision Tree, Discriminant Analysis, Naïve Bayes, Support Vector Machine, k-Nearest Neighbor and Ensemble. The results are compared to the conventional logistic regression method. Both performance criteria and computational times are reported for comparison purposes. A design of experiments is run to test 25 classification methods at ratios of 25%, 50%, and 75% right to left lane change data. Moreover, samples are validated by cross and holdback validation methods. RUS Boosted trees, an ensemble method, shows improvement over logistic regression. This research provides valuable insights on lane change behaviour, including trajectories and driving styles, which falls into the field of microscopic lane change study.