## 4 Experimentation and Analysis  
### Datasets  
To comprehensively evaluate the performance of our framework, three representative datasets were selected: ApolloScapes [41], KITTI [42], the Indian dataset, and a self-collected dataset. Following the criteria outlined in [35], the ApolloScapes, KITTI, and Indian datasets were partitioned into training, testing, and validation sets. This partitioning method was innovatively extended to the self-collected dataset, with specialized data annotation tailored to the task requirements.  
In terms of datasets, the ApolloScapes dataset serves as the core. Its rich driving scenarios, especially overtaking and lane-changing behaviors, are fully demonstrated by the selected 4000 frames of images, providing a solid foundation for model training. We focused on highway and comprehensive road environments for the KITTI dataset, selecting 700 frames from tracking sequences 4, 5, and 10 to evaluate the model's generalization ability under complex road conditions. The Indian dataset, with its unique vehicle types (e.g., tricycles and trucks) and congested driving environments, presents a severe test of the model's transfer learning capability; 600 frames of images provide a small but challenging sample. The 65 km section of the S20 Outer Ring Highway and G2 Beijing-Shanghai Highway from Pudong to Jiading in Shanghai, characterized by high traffic flow and various vehicle types, frequently experiences traffic congestion due to lane-changing collisions, making it an ideal route for data acquisition, as shown in Figure 6. Video captured by the front monocular camera of the data collection vehicle on this road section constituted the experimental dataset.  
Prior to training the network, extensive data preprocessing was performed, including normalizing the image pixel values to the [0, 1] range, adjusting the images to a consistent  
Figure 6: Data collection equipment and collection routes.  
resolution, and applying data enhancement methods (e.g., rotation, flipping, and luminance adjustments) to improve the robustness of the model under varying lighting and environmental conditions. The network training process included dividing the dataset into a training set (70%), validation set (15%), and test set (15%), and training was performed using a cross-entropy loss function and an Adam optimizer with a learning rate of 0.001. The training lasted 300 epochs, and an early stopping strategy was implemented to prevent overfitting. Key hyperparameters include a batch size of 32, a learning rate of 0.001, a discard rate of 0.5 (to mitigate overfitting), and an architecture consisting of five convolutional layers and three fully connected layers.  
At the behavior prediction level, five categories of crucial vehicle behaviors are defined: continuous left-right lane change (CLAL/R), left-right lane change (LCL/LCR), and overtaking (OVT). These categories ensure comprehensive coverage of vehicle dynamic behaviors. Through monocular video processing, the framework was rigorously evaluated using the KITTI, ApolloScapes, and Indian datasets. The selected driving scenarios span various urban and highway conditions and include both standard and non-standard vehicles. This comprehensive evaluation validated the predictive accuracy and adaptability of the model. By integrating multivariate datasets with detailed data annotation, our evaluation framework enhanced the model's predictive ability under complex highway driving conditions and improved its adaptability to various vehicles and environments. This integration lays a solid foundation for developing safer, more efficient autonomous driving systems.