Domain Adaptative Driving Behavior Recognition Through Skeleton-Guided Domain Adversarial Learning.

Driving behavior recognition plays an indispensable role in human-centered intelligent transportation systems. However, the diverse range of scenarios and drivers in practical applications poses a significant challenge for existing methods due to their limited domain generalization ability. To improve the cross-domain performance, we propose a domain adaptive driving behavior recognition method that utilizes skeleton-guided behavior representation and employs graph convolution network (GCN)-assisted domain adversarial learning. First, we propose a novel behavior representation by integrating the driver skeleton with the raw image, which effectively combines high-level behavioral patterns and low-level pixel information to enhance domain invariance. Second, we design a GCN-assisted domain adversarial network that utilizes a graph convolutional network to model the relationships between features of different samples, thereby facilitating more robust domain adaption for driving behavior recognition. Our method outperforms other compared methods in the unsupervised domain adaptation (UDA) tasks across the AUC and State Farm datasets. Moreover, the proposed GCN can serve as a plug-and-play technique to enhance existing unsupervised domain adaptation methods, without the need for additional modifications.