Extracting Overtaking Segments by Unsupervised Clustering and Predicting Nonmotorized Vehicle’s Trajectory

Abstract

Interpretation of flexible cycling behavior has always been a tough task. It is meaningful to understand the overtaking behavior of cyclists for its threats to safety and its high frequency on shared roads. Advanced unsupervised nonparametric clustering methods are compared to distinguish the overtaking segments from the whole trajectory based on the cycling characteristics of nonmotorized two-wheelers, while the hierarchical Dirichlet process hidden Markov model (HDPHMM) outperforms the mixture model via the Dirichlet process (DP mixture model) and topic model via the hierarchical Dirichlet process (HDP topic model). HDPHMM clusters each record into different states and results in more continuous segments. Based on marked vehicle types, which state of clustering model represents the overtaking condition is deduced. The overtaking segments resulted from HDPHMM show the highest homogeneity in cycling features with actual overtaking behavior. Another practical task is to predict the overtaking trajectory and respond to overtaking behavior in advance. Comparing original trajectory and subdivided trajectory, it is found that training model with grouped data, which have homogeneous features, can improve prediction accuracy. With enough trainable samples, CNN + LSTM hybrid structure can achieve trajectory prediction with a mean absolute error of 3 cm. The segmentation produces trajectory segments with similar characteristics. The model is trained with overtaking trajectory segments. With tens of times less trainable data, the prediction on overtaking trajectory still keeps a mean absolute error of about 5 cm. Subdividing trajectory into segments with homogeneous features can improve the prediction accuracy and reduce the requirement of trainable data volume.