###### Abstract
[8] introduced a method based on Domain-Adversarial Recurrent Neural Networks (DA-RNNs), improving the generalization capability of driving manipulation prediction. [PERSON] et al. [4] combined 3D-ResNet with an LSTM to predict driver intent by analyzing driver motion and external vehicle video data. [PERSON] et al. [13] proposed a driver intent prediction method based on monitoring internal and external scenes, achieving better prediction performance with fewer parameters. Analyzing [PERSON] et al. [4] and [PERSON] et al. [13], with both using two branches for internal and external video processing, reveals their distinct approach. While [PERSON] et al. [4] computed optical flow from internal videos, [PERSON] et al. [13] calculated it from external videos. These studies differ from previous approaches that use numerical data (e.g., lane numbers, speed) as external features. Instead, they directly extract external features from external videos by using CNN models. Previously, LSTMs played a crucial role in driver maneuver recognition due to their ability to capture long-distance dependencies. However, LSTMs face challenges in capturing extended dependencies, and they present other challenges, such as high computational complexity, susceptibility to video noise, and interpretability issues. Recent studies favor 3D-CNN models for spatiotemporal feature extraction, addressing LSTMs' limitations. However, learning effective spatiotemporal representations remains a challenge due to local redundancy and global dependence issues. A combination of 3D convolutional neural networks (3D-CNNs) and spatiotemporal transformers has emerged as a promising solution for better driver intent inference. However, both have limitations. While 3D-CNNs reduce spatiotemporal redundancy, their finite receptive fields make learning long-term dependencies difficult. Spatiotemporal transformers excel at capturing global dependencies but introduce redundancy in shallow layers when encoding local spatiotemporal features. Additionally, two challenges affect driver intent inference accuracy. First, inadequate utilization of external video information limits the perception and understanding of the surrounding environment. [PERSON] et al. [13] demonstrated that external videos complement internal driver videos, providing essential information. This external information is necessary to avoid misidentification in challenging situations. Second, the imbalance in training data samples, with straight driving maneuvers being more common than turns and lane changes, poses difficulties in machine learning model training. This imbalance may cause the model to favor dominant classes, reducing accuracy in predicting minority classes. Inspired by transformer models, we propose the Spatial-Temporal Joint Attention Network (STA-Net), combining CNN and transformers in a dual-stream framework. The contributions of this study can be summarized as follows:  
1. We propose a two-stream network to extract in-cabin driver behavior and out-cabin environmental information, addressing spatiotemporal redundancy and insufficient use of driving scene information. 2. We employ the joint learning of the CNN and the transformer to fuse spatiotemporal information at different levels. CNN focuses on low-level local features to reduce redundancy, while the transformer captures high-level global information to address long-term dependencies. 3. We introduce an asymmetric loss function to tackle the problem of imbalanced training data, reducing the negative impact of sample imbalance on model optimization.