Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes

Liu Yang, Maoying Li, Chenyang Shen, Qinghua Hu

Abstract: Driving pattern recognition based on features such as GPS, gear, and speed information is essential to develop intelligent transportation systems. However, it is usually expensive and labor-intensive to collect a large amount of labeled driving data from real-world driving scenes. The lack of labeled data problem in a driving scene substantially hinders the driving pattern recognition accuracy. To handle the scarcity of labeled data, we have developed a novel discriminative transfer learning method for driving pattern recognition to leverage knowledge from related scenes with labeled data to improve recognition performance in unlabeled scenes. Note that data from different scenes may have different distributions, which is a major bottleneck limiting the performance of transfer learning. To address this issue, the proposed method adopts a discriminative distribution matching scheme with the aid of pseudo-labels in unlabeled scenes. It is able to reduce the intra-class distribution disagreement for the same driving pattern among labeled and unlabeled scenes while increasing the inter-class distance among different patterns. Pseudo-labels in unlabeled scenes are updated iteratively via an ensemble strategy that preserves the data structure while enhancing the model robustness. To evaluate the performance of the proposed method, we conducted comprehensive experiments on real-world parking lot datasets. The results show that the proposed method can substantially outperform state-of-the-art methods in driving pattern recognition.