###### Abstract  
Next-generation advanced driver-assistance systems (ADASs) are a promising direction for intelligent transportation systems. To achieve intelligent security monitoring, it is imperative that vehicles possess the ability to accurately comprehend driver maneuvers amidst diverse driver behaviors and complex driving scenarios. Existing CNN-based and transformer-based driver maneuver recognition methods face challenges in effectively capturing global and local features across temporal and spatial dimensions. This paper proposes a Spatial-Temporal Joint Attention Network (STA-Net) to realize high-efficient temporal and spatial feature extractions in driver maneuver recognition. First, we introduce a two-stream architecture for a concurrent analysis of in-cabin driver behaviors and out-cabin environmental information. Second, we propose a Multi-Scale Transposed Attention (MSTA) module and Multi-Scale Feedforward Network (MSFN) to extract features at multiple scales, addressing receptive field inadequacies and combining high-level and low-level information. Third, to address the information redundancy in multi-scale features, we propose a Cross-Spatial Attention Module (CSAM) and Multi-Scale Cross-Spatial Fusion Module (MCFM) to select essential features. Additionally, we introduce an asymmetric loss function to effectively tackle the issue of sample imbalance across diverse categories of driving maneuvers. The proposed method demonstrates a remarkable accuracy of 90.97% and an F1 score of 89.37% on the Brain4 Cars dataset, surpassing the performance of the methods compared. These results substantiate the fact that our approach effectively enhances driver maneuver recognition. driver maneuver recognition; deep learning; multi-scale spatial-temporal attention +
Footnote †: journal: Medical Image and Vision Computing  
In recent years, research has focused on exploring new technologies for comprehensive perception in intelligent vehicles, making it possible to predict driver intent over time. [PERSON] et al. [3] introduced the Autoregressive Input-Output HMM (AIO-HMM) model, which processes both internal (2D facial features) and external features, predicting potential driver actions seconds before driving maneuvers. They also provided the Brain4 Cars dataset, consisting of 1180 miles of natural highway and city driving, for method evaluation. The dataset's video frames contain dynamic information on driver maneuver patterns and road traffic conditions. [PERSON] [4] and [PERSON] [5], among others, conducted statistical analyses on different driving intent expressions, finding a high correlation between driving intent and driver maneuvers. When drivers are about to change their maneuver, they exhibit corresponding actions, such as head posture [3; 6; 7; 8], specific maneuvers, and eye movements while checking the rearview mirror [9; 10], providing crucial evidence for intent inference. This work is significant, as it lays the foundation for driver assistance systems and proposes a method for predicting driver actions using dynamic visual data inside and outside the vehicle. It addresses the challenge of predicting driver operations seconds in advance, allowing for timely warnings to drivers and contributing to the development of next-generation advanced driver-assistance systems (ADASs), reducing road hazard risks. Several researchers have proposed improvements to this pipeline. [PERSON] et al. [11] suggested a deep learning architecture based on recurrent neural networks (RNN-LSTMs), which upgrades internal driver features from 2D to 3D facial features, enhancing the accuracy of driver maneuver prediction by fusing information from multiple sensors. [PERSON] et al. [12] presented a method using driver facial information to predict lane-changing actions, implementing a model based on CNN-LSTMs for analyzing driver actions before lane changes. [PERSON] et al.