## 2 Related Works  
### Driver Maneuver Recognition  
Driver Maneuver Recognition is primarily about intelligent vehicles monitoring the driver's maneuver in real-time. Using machine learning methods, the vehicle analyzes this maneuver data to determine the current actions of the driver and anticipate their future intentions. This enables the provision of various driver assistance features or timely alerts to the driver, thereby enhancing driving safety and efficiency. Current research includes traditional machine learning methods, combinations of 2D CNNs and RNNs, methods combining the 3D CNN and optical flow estimation, and dual-stream frameworks. Here are some representative works:  
Combination of 2D CNNs and RNN Techniques: [PERSON] et al. [14] proposed a method that combines RNN technology with 2D CNN in video processing to handle spatial and temporal information. The 2D CNN is used as an encoder to extract spatial features from the driver's maneuver sequence, and RNN technology serves as a decoder for time modeling to infer the driver's intentions. This method leverages both advantages, improving the efficiency of feature extraction and temporal processing. However, the combined model of 2D CNNs and RNNs is often more complex than using either method alone, leading to challenges in model complexity, increased parameter count, and higher computational resource requirements. Combining 3D CNN and Optical Flow Estimation: [PERSON] et al. [4] proposed a vision-based 3D convolutional residual learning method using optical flow images from the driver's cabin to predict driver intentions. While this method has advantages in capturing spatiotemporal information, accurate maneuver recognition, efficiency, and flexibility in video understanding, it suffers from drawbacks such as numerous parameters, high hardware requirements, large data volume requirements, and sensitivity to lighting and occlusion conditions. Dual-Stream Framework: [PERSON] et al. [13] introduced a ConvLSTM-based autoencoder for extracting vehicle motion information from traffic scenes. They proposed a deep network framework to simultaneously study features from two directions (inside and outside the vehicle) without manual encoding or handcrafted features. This structure achieves advanced driver maneuver prediction performance with fewer parameters than previous works. However, it requires more data to train a finer decoder to interpret long-term motion better, presenting challenges in predicting longer-term motion and computational efficiency due to optical flow estimation. [PERSON] et al. [15] introduce an intelligent vehicle driving intention inference method based on spatiotemporal feature enhancement (STEDII-GRU), aiming to improve the accuracy of driving intention inference. The method first utilizes a pre-trained dual-stream network (SlowFast network) as the backbone for feature extraction. Subsequently, the low frame rate and high frame rate paths are employed to process internal driver behavior video data and external forward traffic scene data. Finally, the joint spatiotemporal features are input into the GRU to obtain the most probable intention. The proposed STEDII-GRU method demonstrates high accuracy in driving intention inference, providing a promising solution for enhancing intelligent vehicles' safety and driving performance. [PERSON] et al. (2016) introduce a novel framework for driver intention prediction. The framework, CEMFormer, employs spatial-temporal transformers to unify memory representations for improved prediction accuracy. It integrates data from both in-cabin and external cameras, enhancing the prediction through historical data fusion and a novel context-consistency loss. [PERSON] et al. (2017) explore a deep neural network framework to anticipate driver maneuvers and enhance takeover readiness in automated driving. Utilizing the Brain4 Cars dataset, the model integrates DenseNet, LSTM, attention mechanisms, and FlowNet2 to predict driver intentions up to 4 s in advance.