## 2 Related Works
### Video Understanding  
The key to understanding driver intent based on visual information lies in accurately interpreting both in-car and out-of-car visual data. In recent years, various deep learning methods for video understanding have been proposed. The purpose of video understanding is to enable computers to interpret and understand video content like humans. Models process videos into sequentially ordered frames, and current video understanding primarily employs recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based architectures to extract and analyze spatiotemporal features from image sequences, ultimately making predictions based on these data.  
Recurrent Neural Networks (RNNs): [PERSON] et al. (2020) proposed a novel dual-stream LSTM architecture called L2 STM for human action recognition in videos, addressing non-stationary dynamics in long-term motion. [PERSON] et al. (2020) introduced the RTPR network architecture for video action detection, utilizing action proposals from previous frames to assist detection in the current frame through a recurrent neural network. In summary, RNNs in video understanding can capture temporal information, handle sequence data, facilitate end-to-end learning, and exhibit high flexibility. However, they also face challenges in training, handling variable-length sequences, dealing with multimodal data, and capturing long-term dependencies.  
Convolutional Neural Networks (CNNs): [PERSON] et al. [23] proposed a novel Two-Stream Inflated 3D ConvNets model (I3D) that extends 2D ConvNets to 3D ConvNets, addressing spatiotemporal modeling issues in video understanding. [PERSON] et al. [24] introduced a new video recognition model, the SlowFast network, which incorporates two different paths (Slow Path and Fast Path) to handle slow and fast video information separately. CNNs in video understanding can effectively capture local patterns and structural information, automatically learn features, and perform parallel computation on large-scale data. However, they also have limitations in capturing global information, dealing with variable-length sequences, having many parameters, and facing imbalances in spatial and temporal information.  
Transformer-based Architectures: [PERSON] et al. [25] presented an algorithm called Long Short-Term Transformer (LSTR) for online action detection, addressing the effective modeling of long-time sequence data and online action detection in videos. [PERSON] et al. [26] proposed a multi-scale visual transformer (MViT) for video and image recognition, connecting the basic idea of a multi-scale feature hierarchy with transformer models. [PERSON] et al. [27] introduced a video classification model, TimeSformer, based on self-attention mechanisms, incorporating Divided Space-Time Attention (T + S) to calculate time and space self-attention scores separately. [PERSON] et al. [28] presented a pure transformer-based video classification model, ViViT, introducing a method called Tubelet Embedding to capture spatiotemporal information in videos effectively.  
While transformer-based models in video understanding have advantages such as capturing long-term dependencies, strong parallel computation capabilities, applicability to multiple tasks, and end-to-end learning, they also come with disadvantages, including high computational resource requirements, limitations on sequence length, high training time, and cost, and a high number of parameters.