Manifold Learning for Lane-changing Behavior Recognition in Urban Traffic

Based on manifold learning (ML), a novel driver behavior recognition (DBR) method is proposed in this paper to recognize the lane-changing behaviors of surrounding vehicles based on the camera-only information. In our study, one of the most widely-used ML methods, isometric mapping (Isomap), is adopted to find the latent manifold structure of the driving data extracted from real-world video frames. Based on the manifold found, the support vector machine (SVM) is applied as a classifier to classify the lane-changing process into three different phases, namely `before lane change' (BLC), `lane change' (LC) and `after lane change' (ALC). After training, different phases can be recognized by SVM. To test the performance of the proposed method, experiments using real-word data are designed and carried out. A linear dimensionality-reduction method, principal component analysis (PCA), is used for comparison. The experimental results verify the ability of ML for finding the low-dimensional structure of data, and compared with PCA, SVM with Isomap shows better performance on the prediction accuracy.